Using X-ray images and deep learning for automated detection of coronavirus disease

Conv-CapsNet: capsule based network for COVID-19 detection through X-Ray scans

Coronavirus, a virus that spread worldwide rapidly and was eventually declared a pandemic. The rapid spread made it essential to detect Coronavirus infected people to control the further spread. Recent studies show that radiological images such as X-Rays and CT scans provide essential information in detecting infection using deep learning models. This paper proposes a shallow architecture based on Capsule Networks with convolutional layers to detect COVID-19 infected persons. The proposed method combines the ability of the capsule network to understand spatial information with convolutional layers for efficient feature extraction. Due to the model's shallow architecture, it has 23M parameters to train and requires fewer training samples. The proposed system is fast and robust and correctly classifies the X-Ray images into three classes, i.e. COVID-19, No Findings, and Viral Pneumonia. Experimental results on the X-Ray dataset show that our model performs well despite having fewer samples for the training and achieved an average accuracy of 96.47% for multi-class and 97.69% for binary classification on 5-fold cross-validation. The proposed model would be useful to researchers and medical professionals for assistance and prognosis for COVID-19 infected patients.

SecureFed: federated learning empowered medical imaging technique to analyze lung abnormalities in chest X-rays

Machine learning is an effective and accurate technique to diagnose COVID-19 infections using image data, and chest X-Ray (CXR) is no exception. Considering privacy issues, machine learning scientists end up receiving less medical imaging data. Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm that generates an unbiased global model that follows local model (from clients) without exposing their personal data. In the case of heterogeneous data among clients, vanilla or default FL mechanism still introduces an insecure method for updating models. Therefore, we proposed SecureFed-a secure aggregation method-which ensures fairness and robustness. In our experiments, we employed COVID-19 CXR dataset (of size 2100 positive cases) and compared it with the existing FL frameworks such as FedAvg, FedMGDA+, and FedRAD. In our comparison, we primarily considered robustness (accuracy) and fairness (consistency). As the SecureFed produced consistently better results, it is generic enough to be considered for multimodal data.

Novel Light Convolutional Neural Network for COVID Detection with Watershed Based Region Growing Segmentation

A rapidly spreading epidemic, COVID-19 had a serious effect on millions and took many lives. Therefore, for individuals with COVID-19, early discovery is essential for halting the infection's progress. To quickly and accurately diagnose COVID-19, imaging modalities, including computed tomography (CT) scans and chest X-ray radiographs, are frequently employed. The potential of artificial intelligence (AI) approaches further explored the creation of automated and precise COVID-19 detection systems. Scientists widely use deep learning techniques to identify coronavirus infection in lung imaging. In our paper, we developed a novel light CNN model architecture with watershed-based region-growing segmentation on Chest X-rays. Both CT scans and X-ray radiographs were employed along with 5-fold cross-validation. Compared to earlier state-of-the-art models, our model is lighter and outperformed the previous methods by achieving a mean accuracy of 98.8% on X-ray images and 98.6% on CT scans, predicting the rate of 0.99% and 0.97% for PPV (Positive predicted Value) and NPV (Negative predicted Value) rate of 0.98% and 0.99%, respectively.

Fast Image Super-Resolution Using Particle Swarm Optimization-Based Convolutional Neural Networks

Image super-resolution based on convolutional neural networks (CNN) is a hot topic in image processing. However, image super-resolution faces significant challenges in practical applications. Improving its performance on lightweight architectures is important for real-time super-resolution. In this paper, a joint algorithm consisting of modified particle swarm optimization (SMCPSO) and fast super-resolution convolutional neural networks (FSRCNN) is proposed. In addition, a mutation mechanism for particle swarm optimization (PSO) was obtained. Specifically, the SMCPSO algorithm was introduced to optimize the weights and bias of the CNNs, and the aggregation degree of the particles was adjusted adaptively by a mutation mechanism to ensure the global searching ability of the particles and the diversity of the population. The results showed that SMCPSO-FSRCNN achieved the most significant improvement, being about 4.84% better than the FSRCNN model, using the BSD100 data set at a scale factor of 2. In addition, a chest X-ray super-resolution images classification test experiment was conducted, and the experimental results demonstrated that the reconstruction ability of this model could improve the classification accuracy by 13.46%; in particular, the precision and recall rate of COVID-19 were improved by 45.3% and 6.92%, respectively.

CoVSeverity-Net: an efficient deep learning model for COVID-19 severity estimation from Chest X-Ray images

Purpose

COVID-19 is not going anywhere and is slowly becoming a part of our life. The World Health Organization declared it a pandemic in 2020, and it has affected all of us in many ways. Several deep learning techniques have been developed to detect COVID-19 from Chest X-Ray images. COVID-19 infection severity scoring can aid in establishing the optimum course of treatment and care for a positive patient, as all COVID-19 positive patients do not require special medical attention. Still, very few works are reported to estimate the severity of the disease from the Chest X-Ray images. The unavailability of the large-scale dataset might be a reason.

Methods

We aim to propose CoVSeverity-Net, a deep learning-based architecture for predicting the severity of COVID-19 from Chest X-ray images. CoVSeverity-Net is trained on a public COVID-19 dataset, curated by experienced radiologists for severity estimation. For that, a large publicly available dataset is collected and divided into three levels of severity, namely Mild, Moderate, and Severe.

Results

An accuracy of 85.71% is reported. Conducting 5-fold cross-validation, we have obtained an accuracy of 87.82 ± 6.25%. Similarly, conducting 10-fold cross-validation we obtained accuracy of 91.26 ± 3.42. The results were better when compared with other state-of-the-art architectures.

Conclusion

We strongly believe that this study has a high chance of reducing the workload of overworked front-line radiologists, speeding up patient diagnosis and treatment, and easing pandemic control. Future work would be to train a novel deep learning-based architecture on a larger dataset for severity estimation.

Evaluating deep learning predictions for COVID-19 from X-ray images using leave-one-out predictive densities

Early detection of the COVID-19 virus is an important task for controlling the spread of the pandemic. Imaging techniques such as chest X-ray are relatively inexpensive and accessible, but its interpretation requires expert knowledge to evaluate the disease severity. Several approaches for automatic COVID-19 detection using deep learning techniques have been proposed. While most approaches show high accuracy on the COVID-19 detection task, there is not enough evidence on external evaluation for this technique. Furthermore, data scarcity and sampling biases make difficult to properly evaluate model predictions. In this paper, we propose stochastic gradient Langevin dynamics (SGLD) to take into account the model uncertainty. Four different deep learning architectures are trained using SGLD and compared to their baselines using stochastic gradient descent. The model uncertainties are also evaluated according to their convergence properties and the leave-one-out predictive densities. The proposed approach is able to reduce overconfidence of the baseline estimators while also retaining predictive accuracy for the best-performing cases.

In Search of an Efficient and Reliable Deep Learning Model for Identification of COVID-19 Infection from Chest X-ray Images

The virus responsible for COVID-19 is mutating day by day with more infectious characteristics. With the limited healthcare resources and overburdened medical practitioners, it is almost impossible to contain this virus. The automatic identification of this viral infection from chest X-ray (CXR) images is now more demanding as it is a cheaper and less time-consuming diagnosis option. To that cause, we have applied deep learning (DL) approaches for four-class classification of CXR images comprising COVID-19, normal, lung opacity, and viral pneumonia. At first, we extracted features of CXR images by applying a local binary pattern (LBP) and pre-trained convolutional neural network (CNN). Afterwards, we utilized a pattern recognition network (PRN), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbors (KNN) classifiers on the extracted features to classify aforementioned four-class CXR images. The performances of the proposed methods have been analyzed rigorously in terms of classification performance and classification speed. Among different methods applied to the four-class test images, the best method achieved classification performances with 97.41% accuracy, 94.94% precision, 94.81% recall, 98.27% specificity, and 94.86% F1 score. The results indicate that the proposed method can offer an efficient and reliable framework for COVID-19 detection from CXR images, which could be immensely conducive to the effective diagnosis of COVID-19-infected patients.

Introduction to radiomics for a clinical audience

Radiomics is a rapidly developing field of research focused on the extraction of quantitative features from medical images, thus converting these digital images into minable, high-dimensional data, which offer unique biological information that can enhance our understanding of disease processes and provide clinical decision support. To date, most radiomics research has been focused on oncological applications; however, it is increasingly being used in a raft of other diseases. This review gives an overview of radiomics for a clinical audience, including the radiomics pipeline and the common pitfalls associated with each stage. Key studies in oncology are presented with a focus on both those that use radiomics analysis alone and those that integrate its use with other multimodal data streams. Importantly, clinical applications outside oncology are also presented. Finally, we conclude by offering a vision for radiomics research in the future, including how it might impact our practice as radiologists.

An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis

Coronavirus Disease-2019 (COVID-19) causes Severe Acute Respiratory Syndrome-Corona Virus-2 (SARS-CoV-2) and has opened several challenges for research concerning diagnosis and treatment. Chest X-rays and computed tomography (CT) scans are effective and fast alternatives to detect and assess the damage that COVID causes to the lungs at different stages of the disease. Although the CT scan is an accurate exam, the chest X-ray is still helpful due to the cheaper, faster, lower radiation exposure, and is available in low-incoming countries. Computer-aided diagnostic systems based on Artificial Intelligence (AI) and computer vision are an alternative to extract features from X-ray images, providing an accurate COVID-19 diagnosis. However, specialized and expensive computational resources come across as challenging. Also, it needs to be better understood how low-cost devices and smartphones can hold AI models to predict diseases timely. Even using deep learning to support image-based medical diagnosis, challenges still need to be addressed once the known techniques use centralized intelligence on high-performance servers, making it difficult to embed these models in low-cost devices. This paper sheds light on these questions by proposing the Artificial Intelligence as a Service Architecture (AIaaS), a hybrid AI support operation, both centralized and distributed, with the purpose of enabling the embedding of already-trained models on low-cost devices or smartphones. We demonstrated the suitability of our architecture through a case study of COVID-19 diagnosis using a low-cost device. Among the main findings of this paper, we point out the performance evaluation of low-cost devices to handle COVID-19 predicting tasks timely and accurately and the quantitative performance evaluation of CNN models embodiment on low-cost devices.

Interpretable Differential Diagnosis of Non-COVID Viral Pneumonia, Lung Opacity and COVID-19 Using Tuned Transfer Learning and Explainable AI

The coronavirus epidemic has spread to virtually every country on the globe, inflicting enormous health, financial, and emotional devastation, as well as the collapse of healthcare systems in some countries. Any automated COVID detection system that allows for fast detection of the COVID-19 infection might be highly beneficial to the healthcare service and people around the world. Molecular or antigen testing along with radiology X-ray imaging is now utilized in clinics to diagnose COVID-19. Nonetheless, due to a spike in coronavirus and hospital doctors' overwhelming workload, developing an AI-based auto-COVID detection system with high accuracy has become imperative. On X-ray images, the diagnosis of COVID-19, non-COVID-19 non-COVID viral pneumonia, and other lung opacity can be challenging. This research utilized artificial intelligence (AI) to deliver high-accuracy automated COVID-19 detection from normal chest X-ray images. Further, this study extended to differentiate COVID-19 from normal, lung opacity and non-COVID viral pneumonia images. We have employed three distinct pre-trained models that are Xception, VGG19, and ResNet50 on a benchmark dataset of 21,165 X-ray images. Initially, we formulated the COVID-19 detection problem as a binary classification problem to classify COVID-19 from normal X-ray images and gained 97.5%, 97.5%, and 93.3% accuracy for Xception, VGG19, and ResNet50 respectively. Later we focused on developing an efficient model for multi-class classification and gained an accuracy of 75% for ResNet50, 92% for VGG19, and finally 93% for Xception. Although Xception and VGG19's performances were identical, Xception proved to be more efficient with its higher precision, recall, and f-1 scores. Finally, we have employed Explainable AI on each of our utilized model which adds interpretability to our study. Furthermore, we have conducted a comprehensive comparison of the model's explanations and the study revealed that Xception is more precise in indicating the actual features that are responsible for a model's predictions.This addition of explainable AI will benefit the medical professionals greatly as they will get to visualize how a model makes its prediction and won't have to trust our developed machine-learning models blindly.

CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images

The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible to provide appropriate treatment for patients and prevent the pandemic from spreading further. Both chest X-ray and computed tomography (CT) images are capable of accurately diagnosing COVID-19. To distinguish lung illnesses (i.e., COVID-19 and pneumonia) from normal cases using chest X-ray and CT images, we combined convolutional neural network (CNN) and recurrent neural network (RNN) models by replacing the fully connected layers of CNN with a version of RNN. In this framework, the attributes of CNNs were utilized to extract features and those of RNNs to calculate dependencies and classification base on extracted features. CNN models VGG19, ResNet152V2, and DenseNet121 were combined with long short-term memory (LSTM) and gated recurrent unit (GRU) RNN models, which are convenient to develop because these networks are all available as features on many platforms. The proposed method is evaluated using a large dataset totaling 16,210 X-ray and CT images (5252 COVID-19 images, 6154 pneumonia images, and 4804 normal images) were taken from several databases, which had various image sizes, brightness levels, and viewing angles. Their image quality was enhanced via normalization, gamma correction, and contrast-limited adaptive histogram equalization. The ResNet152V2 with GRU model achieved the best architecture with an accuracy of 93.37%, an F1 score of 93.54%, a precision of 93.73%, and a recall of 93.47%. From the experimental results, the proposed method is highly effective in distinguishing lung diseases. Furthermore, both CT and X-ray images can be used as input for classification, allowing for the rapid and easy detection of COVID-19.

Weighted ensemble model for image classification

The Deep Convolutional Neural Network (DCNN) classification models are being tremendously used across many research fields including medical science for image classification. The accuracy of the model and reliability on the results of the model are the key attributes which determine whether a particular model should be used for a specific application or not. A highly accurate model is always desirable for all applications of machine learning as well as deep learning. This paper presents a DCNN based heterogeneous ensemble approach where all DCNN models can be trained on a single dataset and each model can contribute of towards the final output of the ensemble model. The contribution of each model is weighted according to its individual accuracy on the given dataset. Models with higher accuracy has higher contribution in the final output of ensemble model, whereas the models with lower accuracy has lower contribution. This approach, when tested on two different X-ray images datasets of Covid-19, has confirmed the significant increase in 3-class accuracy as compared to the models in literature.

COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods

Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems of humans, there is a need for chest radiographic imaging (CXR) for monitoring the disease and preventing further deaths. Several studies have been shown that Deep Learning models can achieve promising results for COVID-19 diagnosis towards the CXR perspective. In this study, five deep learning models were analyzed and evaluated with the aim of identifying COVID-19 from chest X-ray images. The scope of this study is to highlight the significance and potential of individual deep learning models in COVID-19 CXR images. More specifically, we utilized the ResNet50, ResNet101, DenseNet121, DenseNet169 and InceptionV3 using Transfer Learning. All models were trained and validated on the largest publicly available repository for COVID-19 CXR images. Furthermore, they were evaluated on unknown data that was not used for training or validation, authenticating their performance and clarifying their usage in a medical scenario. All models achieved satisfactory performance where ResNet101 was the superior model achieving 96% in Precision, Recall and Accuracy, respectively. Our outcomes show the potential of deep learning models on COVID-19 medical offering a promising way for the deeper understanding of COVID-19.

A multi-output network with U-net enhanced class activation map and robust classification performance for medical imaging analysis

Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image-to-label result provide insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. To gain local insight of cancerous regions, separate tasks such as imaging segmentation needs to be implemented to aid the doctors in treating patients which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive the AI-first medical solutions further, this paper proposes a multi-output network which follows a U-Net architecture for image segmentation output and features an additional CNN module for auxiliary classification output. Class Activation Maps or CAMs are a method of providing insight into a convolutional neural network’s feature maps that lead to its classification but in the case of lung diseases, the region of interest is enhanced by U-net assisted Class Activation Mapping (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and can generate classification results simultaneously which builds trust for AI-led diagnosis system. The proposed U-Net model achieves 97.72% accuracy and a dice coefficient of 0.9691 on a testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

A Holistic Approach to Identify and Classify COVID-19 from Chest Radiographs, ECG, and CT-Scan Images Using ShuffleNet Convolutional Neural Network

Early and precise COVID-19 identification and analysis are pivotal in reducing the spread of COVID-19. Medical imaging techniques, such as chest X-ray or chest radiographs, computed tomography (CT) scan, and electrocardiogram (ECG) trace images are the most widely known for early discovery and analysis of the coronavirus disease (COVID-19). Deep learning (DL) frameworks for identifying COVID-19 positive patients in the literature are limited to one data format, either ECG or chest radiograph images. Moreover, using several data types to recover abnormal patterns caused by COVID-19 could potentially provide more information and restrict the spread of the virus. This study presents an effective COVID-19 detection and classification approach using the Shufflenet CNN by employing three types of images, i.e., chest radiograph, CT-scan, and ECG-trace images. For this purpose, we performed extensive classification experiments with the proposed approach using each type of image. With the chest radiograph dataset, we performed three classification experiments at different levels of granularity, i.e., binary, three-class, and four-class classifications. In addition, we performed a binary classification experiment with the proposed approach by classifying CT-scan images into COVID-positive and normal. Finally, utilizing the ECG-trace images, we conducted three experiments at different levels of granularity, i.e., binary, three-class, and five-class classifications. We evaluated the proposed approach with the baseline COVID-19 Radiography Database, SARS-CoV-2 CT-scan, and ECG images dataset of cardiac and COVID-19 patients. The average accuracy of 99.98% for COVID-19 detection in the three-class classification scheme using chest radiographs, optimal accuracy of 100% for COVID-19 detection using CT scans, and average accuracy of 99.37% for five-class classification scheme using ECG trace images have proved the efficacy of our proposed method over the contemporary methods. The optimal accuracy of 100% for COVID-19 detection using CT scans and the accuracy gain of 1.54% (in the case of five-class classification using ECG trace images) from the previous approach, which utilized ECG images for the first time, has a major contribution to improving the COVID-19 prediction rate in early stages. Experimental findings demonstrate that the proposed framework outperforms contemporary models. For example, the proposed approach outperforms state-of-the-art DL approaches, such as Squeezenet, Alexnet, and Darknet19, by achieving the accuracy of 99.98 (proposed method), 98.29, 98.50, and 99.67, respectively.

LDDNet: A Deep Learning Framework for the Diagnosis of Infectious Lung Diseases

This paper proposes a new deep learning (DL) framework for the analysis of lung diseases, including COVID-19 and pneumonia, from chest CT scans and X-ray (CXR) images. This framework is termed optimized DenseNet201 for lung diseases (LDDNet). The proposed LDDNet was developed using additional layers of 2D global average pooling, dense and dropout layers, and batch normalization to the base DenseNet201 model. There are 1024 Relu-activated dense layers and 256 dense layers using the sigmoid activation method. The hyper-parameters of the model, including the learning rate, batch size, epochs, and dropout rate, were tuned for the model. Next, three datasets of lung diseases were formed from separate open-access sources. One was a CT scan dataset containing 1043 images. Two X-ray datasets comprising images of COVID-19-affected lungs, pneumonia-affected lungs, and healthy lungs exist, with one being an imbalanced dataset with 5935 images and the other being a balanced dataset with 5002 images. The performance of each model was analyzed using the Adam, Nadam, and SGD optimizers. The best results have been obtained for both the CT scan and CXR datasets using the Nadam optimizer. For the CT scan images, LDDNet showed a COVID-19-positive classification accuracy of 99.36%, a 100% precision recall of 98%, and an F1 score of 99%. For the X-ray dataset of 5935 images, LDDNet provides a 99.55% accuracy, 73% recall, 100% precision, and 85% F1 score using the Nadam optimizer in detecting COVID-19-affected patients. For the balanced X-ray dataset, LDDNet provides a 97.07% classification accuracy. For a given set of parameters, the performance results of LDDNet are better than the existing algorithms of ResNet152V2 and XceptionNet.

COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks

X-ray is a useful imaging modality widely utilized for diagnosing COVID-19 virus that infected a high number of people all around the world. The manual examination of these X-ray images may cause problems especially when there is lack of medical staff. Usage of deep learning models is known to be helpful for automated diagnosis of COVID-19 from the X-ray images. However, the widely used convolutional neural network architectures typically have many layers causing them to be computationally expensive. To address these problems, this study aims to design a lightweight differential diagnosis model based on convolutional neural networks. The proposed model is designed to classify the X-ray images belonging to one of the four classes that are Healthy, COVID-19, viral pneumonia, and bacterial pneumonia. To evaluate the model performance, accuracy, precision, recall, and F1-Score were calculated. The performance of the proposed model was compared with those obtained by applying transfer learning to the widely used convolutional neural network models. The results showed that the proposed model with low number of computational layers outperforms the pre-trained benchmark models, achieving an accuracy value of 89.89% while the best pre-trained model (Efficient-Net B2) achieved accuracy of 85.7%. In conclusion, the proposed lightweight model achieved the best overall result in classifying lung diseases allowing it to be used on devices with limited computational power. On the other hand, all the models showed a poor precision on viral pneumonia class and confusion in distinguishing it from bacterial pneumonia class, thus a decrease in the overall accuracy.

CovidViT: a novel neural network with self-attention mechanism to detect Covid-19 through X-ray images

Since the emergence of the novel coronavirus in December 2019, it has rapidly swept across the globe, with a huge impact on daily life, public health and the economy around the world. There is an urgent necessary for a rapid and economical detection method for the Covid-19. In this study, we used the transformers-based deep learning method to analyze the chest X-rays of normal, Covid-19 and viral pneumonia patients. Covid-Vision-Transformers (CovidViT) is proposed to detect Covid-19 cases through X-ray images. CovidViT is based on transformers block with the self-attention mechanism. In order to demonstrate its superiority, this research is also compared with other popular deep learning models, and the experimental result shows CovidViT outperforms other deep learning models and achieves 98.0% accuracy on test set, which means that the proposed model is excellent in Covid-19 detection. Besides, an online system for quick Covid-19 diagnosis is built on http://yanghang.site/covid19.

AI-based radiodiagnosis using chest X-rays: A review

Chest Radiograph or Chest X-ray (CXR) is a common, fast, non-invasive, relatively cheap radiological examination method in medical sciences. CXRs can aid in diagnosing many lung ailments such as Pneumonia, Tuberculosis, Pneumoconiosis, COVID-19, and lung cancer. Apart from other radiological examinations, every year, 2 billion CXRs are performed worldwide. However, the availability of the workforce to handle this amount of workload in hospitals is cumbersome, particularly in developing and low-income nations. Recent advances in AI, particularly in computer vision, have drawn attention to solving challenging medical image analysis problems. Healthcare is one of the areas where AI/ML-based assistive screening/diagnostic aid can play a crucial part in social welfare. However, it faces multiple challenges, such as small sample space, data privacy, poor quality samples, adversarial attacks and most importantly, the model interpretability for reliability on machine intelligence. This paper provides a structured review of the CXR-based analysis for different tasks, lung diseases and, in particular, the challenges faced by AI/ML-based systems for diagnosis. Further, we provide an overview of existing datasets, evaluation metrics for different[][15mm][0mm]Q5 tasks and patents issued. We also present key challenges and open problems in this research domain.

A lightweight deep learning approach for COVID-19 detection using X-ray images with edge federation

Objective

This study aims to develop a lightweight convolutional neural network-based edge federated learning architecture for COVID-19 detection using X-ray images, aiming to minimize computational cost, latency, and bandwidth requirements while preserving patient privacy.

Method

The proposed method uses an edge federated learning architecture to optimize task allocation and execution. Unlike in traditional edge networks where requests from fixed nodes are handled by nearby edge devices or remote clouds, the proposed model uses an intelligent broker within the federation to assess member edge cloudlets' parameters, such as resources and hop count, to make optimal decisions for task offloading. This approach enhances performance and privacy by placing tasks in closer proximity to the user. DenseNet is used for model training, with a depth of 60 and 357,482 parameters. This resource-aware distributed approach optimizes computing resource utilization within the edge-federated learning architecture.

Results

The experimental results demonstrate significant improvements in various performance metrics. The proposed method reduces training time by 53.1%, optimizes CPU and memory utilization by 17.5% and 33.6%, and maintains accurate COVID-19 detection capabilities without compromising the F1 score, demonstrating the efficiency and effectiveness of the lightweight convolutional neural network-based edge federated learning architecture.

Conclusion

Existing studies predominantly concentrate on either privacy and accuracy or load balancing and energy optimization, with limited emphasis on training time. The proposed approach offers a comprehensive performance-centric solution that simultaneously addresses privacy, load balancing, and energy optimization while reducing training time, providing a more holistic and balanced solution for optimal system performance.

Machine Learning and COVID-19: Lessons from SARS-CoV-2

Currently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.

Detection of Covid-19 and other pneumonia cases from CT and X-ray chest images using deep learning based on feature reuse residual block and depthwise dilated convolutions neural network

Covid-19 has become a worldwide epidemic which has caused the death of millions in a very short time. This disease, which is transmitted rapidly, has mutated and different variations have emerged. Early diagnosis is important to prevent the spread of this disease. In this study, a new deep learning-based architecture is proposed for rapid detection of Covid-19 and other symptoms using CT and X-ray chest images. This method, called CovidDWNet, is based on a structure based on feature reuse residual block (FRB) and depthwise dilated convolutions (DDC) units. The FRB and DDC units efficiently acquired various features in the chest scan images and it was seen that the proposed architecture significantly improved its performance. In addition, the feature maps obtained with the CovidDWNet architecture were estimated with the Gradient boosting (GB) algorithm. With the CovidDWNet+GB architecture, which is a combination of CovidDWNet and GB, a performance increase of approximately 7% in CT images and between 3% and 4% in X-ray images has been achieved. The CovidDWNet+GB architecture achieved the highest success compared to other architectures, with 99.84% and 100% accuracy rates, respectively, on different datasets containing binary class (Covid-19 and Normal) CT images. Similarly, the proposed architecture showed the highest success with 96.81% accuracy in multi-class (Covid-19, Lung Opacity, Normal and Viral Pneumonia) X-ray images and 96.32% accuracy in the dataset containing X-ray and CT images. When the time to predict the disease in CT or X-ray images is examined, it is possible to say that it has a high speed because the CovidDWNet+GB method predicts thousands of images within seconds.

Low-rank robust online distance/similarity learning based on the rescaled hinge loss

An important challenge in metric learning is scalability to both size and dimension of input data. Online metric learning algorithms are proposed to address this challenge. Existing methods are commonly based on Passive/Aggressive (PA) approach. Hence, they can rapidly process large volumes of data with an adaptive learning rate. However, these algorithms are based on the Hinge loss and so are not robust against outliers and label noise. We address the challenges by formulating the online Distance/Similarity learning problem with the robust Rescaled Hinge loss function. The proposed model is rather general and can be applied to any PA-based online Distance/Similarity algorithm. To achieve scalability to data dimension, we propose low-rank online Distance/Similarity methods that learn a rectangular projection matrix instead of a full Mahalanobis matrix. The low-rank approaches not only reduce the computational cost but also keep the discrimination power of the learned metrics. Also, current online methods usually assume training triplets or pairwise constraints exist in advance. However, this assumption does not hold, and generating triplets using available batch sampling methods is both time and space consuming. We address this issue by developing an efficient, yet effective robust one-pass triplet construction algorithm. We conduct several experiments on datasets from various applications. The results confirm that the proposed methods significantly outperform state-of-the-art online metric learning methods in the presence of label noise and outliers by a large margin.

A two-tier feature selection method using Coalition game and Nystrom sampling for screening COVID-19 from chest X-Ray images

The world is still under the threat of different strains of the coronavirus and the pandemic situation is far from over. The method, that is widely used for the detection of COVID-19 is Reverse Transcription Polymerase chain reaction (RT-PCR), which is a time-consuming method and is prone to manual errors, and has poor precision. Although many nations across the globe have begun the mass immunization procedure, the COVID-19 vaccine will take a long time to reach everyone. The application of artificial intelligence (AI) and computer-aided diagnosis (CAD) has been used in the domain of medical imaging for a long period. It is quite evident that the use of CAD in the detection of COVID-19 is inevitable. The main objective of this paper is to use convolutional neural network (CNN) and a novel feature selection technique to analyze Chest X-Ray (CXR) images for the detection of COVID-19. We propose a novel two-tier feature selection method, which increases the accuracy of the overall classification model used for screening COVID-19 CXRs. Filter feature selection models are often more effective than wrapper methods as wrapper methods tend to be computationally more expensive and are not useful for large datasets dealing with a large number of features. However, most filter methods do not take into consideration how a group of features would work together, rather they just look at the features individually and decide on a score. We have used approximate Shapley value, a concept of Coalition game theory, to deal with this problem. Further, in the case of a large dataset, it is important to work with shorter embeddings of the features. We have used CUR decomposition and Nystrom sampling to further reduce the feature space. To check the efficacy of this two-tier feature selection method, we have applied it to the features extracted by three standard deep learning models, namely VGG16, Xception and InceptionV3, where the features have been extracted from the CXR images of COVID-19 datasets and we have found that the selection procedure works quite well for the features extracted by Xception and InceptionV3. The source code of this work is available at https://github.com/subhankar01/covidfs-aihc.

Harnessing intelligent technologies to curb COVID-19 pandemic: taxonomy and open challenges

The world has changed dramatically since the outbreak of COVID-19 pandemic. This has not only affected the humanity, but has also badly damaged the world’s socio-economic system. Currently, people are looking for a magical solution to overcome this pandemic. Similarly, scientists across the globe are working to find remedies to overcome this challenge. The role of technologies is not far behind in this situation, which attracts many sectors from government agencies to medical practitioners, and market analysts. This is quite true that in a few months of time, scientists, researchers, and industrialists have come up with some acceptable innovative solutions and harnessing existing technologies to stop the spread of COVID-19. Therefore, it is pertinent to highlight the role of intelligent technologies, which play a pivotal role in curbing this pandemic. In this paper, we devise a taxonomy related to the technologies being used in the current pandemic. We show that the most prominent technologies are artificial intelligence, machine learning, cloud computing, big data analytics, and blockchain. Moreover, we highlight some key open challenges, which technologists might face to control this outbreak. Finally, we conclude that to impede this pandemic, a collective effort is required from different professionals in support of using existing and new technologies. Finally, we conclude that to stop this pandemic, machine learning approaches with integration of cloud computing using high performance computing could provision the pandemic with minimum cost and time.

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

Background

Chest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering.

Material and method

We used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm.

Results

Swin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity.

Conclusions

Our handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

COVID-19 Detection: A Systematic Review of Machine and Deep Learning-Based Approaches Utilizing Chest X-Rays and CT Scans

This review study presents the state-of-the-art machine and deep learning-based COVID-19 detection approaches utilizing the chest X-rays or computed tomography (CT) scans. This study aims to systematically scrutinize as well as to discourse challenges and limitations of the existing state-of-the-art research published in this domain from March 2020 to August 2021. This study also presents a comparative analysis of the performance of four majorly used deep transfer learning (DTL) models like VGG16, VGG19, ResNet50, and DenseNet over the COVID-19 local CT scans dataset and global chest X-ray dataset. A brief illustration of the majorly used chest X-ray and CT scan datasets of COVID-19 patients utilized in state-of-the-art COVID-19 detection approaches are also presented for future research. The research databases like IEEE Xplore, PubMed, and Web of Science are searched exhaustively for carrying out this survey. For the comparison analysis, four deep transfer learning models like VGG16, VGG19, ResNet50, and DenseNet are initially fine-tuned and trained using the augmented local CT scans and global chest X-ray dataset in order to observe their performance. This review study summarizes major findings like AI technique employed, type of classification performed, used datasets, results in terms of accuracy, specificity, sensitivity, F1 score, etc., along with the limitations, and future work for COVID-19 detection in tabular manner for conciseness. The performance analysis of the four majorly used deep transfer learning models affirms that Visual Geometry Group 19 (VGG19) model delivered the best performance over both COVID-19 local CT scans dataset and global chest X-ray dataset.

A Survey on Deep Learning in COVID-19 Diagnosis

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

Preliminary Stages for COVID-19 Detection Using Image Processing

COVID-19 was first discovered in December 2019 in Wuhan. There have been reports of thousands of illnesses and hundreds of deaths in almost every region of the world. Medical images, when combined with cutting-edge technology such as artificial intelligence, have the potential to improve the efficiency of the public health system and deliver faster and more reliable findings in the detection of COVID-19. The process of developing the COVID-19 diagnostic system begins with image accusation and proceeds via preprocessing, feature extraction, and classification. According to literature review, several attempts to develop taxonomies for COVID-19 detection using image processing methods have been introduced. However, most of these adhere to a standard category that exclusively considers classification methods. Therefore, in this study a new taxonomy for the early stages of COVID-19 detection is proposed. It attempts to offer a full grasp of image processing in COVID-19 while considering all phases required prior to classification. The survey concludes with a discussion of outstanding concerns and future directions.

A few-shot approach for COVID-19 screening in standard and portable chest X-ray images

Reliable and effective diagnostic systems are of vital importance for COVID-19, specifically for triage and screening procedures. In this work, a fully automatic diagnostic system based on chest X-ray images (CXR) has been proposed. It relies on the few-shot paradigm, which allows to work with small databases. Furthermore, three components have been added to improve the diagnosis performance: (1) a region proposal network which makes the system focus on the lungs; (2) a novel cost function which adds expert knowledge by giving specific penalties to each misdiagnosis; and (3) an ensembling procedure integrating multiple image comparisons to produce more reliable diagnoses. Moreover, the COVID-SC dataset has been introduced, comprising almost 1100 AnteroPosterior CXR images, namely 439 negative and 653 positive according to the RT-PCR test. Expert radiologists divided the negative images into three categories (normal lungs, COVID-related diseases, and other diseases) and the positive images into four severity levels. This entails the most complete COVID-19 dataset in terms of patient diversity. The proposed system has been compared with state-of-the-art methods in the COVIDGR-1.0 public database, achieving the highest accuracy (81.13% ± 2.76%) and the most robust results. An ablation study proved that each system component contributes to improve the overall performance. The procedure has also been validated on the COVID-SC dataset under different scenarios, with accuracies ranging from 70.81 to 87.40%. In conclusion, our proposal provides a good accuracy appropriate for the early detection of COVID-19.

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia. Delayed diagnosis and overpriced tests are the prime reason for the high mortality rate, especially in underdeveloped countries. A time and cost-efficient diagnosis tool: Chest X-rays, was thus accepted as the standard diagnostic test for pediatric pneumonia. However, the lower radiation levels for diagnosis in children make the task much more onerous and time-consuming. The mentioned challenges initiate the need for a computer-aided detection model that is instantaneous and accurate. Our work proposes a stacked ensemble learning of deep learning-based features for pediatric pneumonia classification. The extracted features from the global average pooling layer of the fine-tuned Xception model pretrained on ImageNet weights are sent to the Kernel Principal Component Analysis for dimensionality reduction. The dimensionally reduced features are further trained and validated on the stacking classifier. The stacking classifier consists of two stages; the first stage uses the Random-Forest classifier, K-Nearest Neighbors, Logistic Regression, XGB classifier, Support Vector Classifier (SVC), Nu-SVC, and MLP classifier. The second stage operates on Logistic Regression using the first stage predictions for the final classification with Stratified K-fold cross-validation to prevent overfitting. The model was tested on the publicly available pediatric pneumonia dataset, achieving an accuracy of 98.3%, precision of 99.29%, recall of 98.36%, F1-score of 98.83%, and an AUC score of 98.24%. The performance shows its reliability for real-time deployment in assisting radiologists and physicians.

A Survey on AI Techniques for Thoracic Diseases Diagnosis Using Medical Images

Thoracic diseases refer to disorders that affect the lungs, heart, and other parts of the rib cage, such as pneumonia, novel coronavirus disease (COVID-19), tuberculosis, cardiomegaly, and fracture. Millions of people die every year from thoracic diseases. Therefore, early detection of these diseases is essential and can save many lives. Earlier, only highly experienced radiologists examined thoracic diseases, but recent developments in image processing and deep learning techniques are opening the door for the automated detection of these diseases. In this paper, we present a comprehensive review including: types of thoracic diseases; examination types of thoracic images; image pre-processing; models of deep learning applied to the detection of thoracic diseases (e.g., pneumonia, COVID-19, edema, fibrosis, tuberculosis, chronic obstructive pulmonary disease (COPD), and lung cancer); transfer learning background knowledge; ensemble learning; and future initiatives for improving the efficacy of deep learning models in applications that detect thoracic diseases. Through this survey paper, researchers may be able to gain an overall and systematic knowledge of deep learning applications in medical thoracic images. The review investigates a performance comparison of various models and a comparison of various datasets.

Dual_Pachi: Attention-based dual path framework with intermediate second order-pooling for Covid-19 detection from chest X-ray images

Numerous machine learning and image processing algorithms, most recently deep learning, allow the recognition and classification of COVID-19 disease in medical images. However, feature extraction, or the semantic gap between low-level visual information collected by imaging modalities and high-level semantics, is the fundamental shortcoming of these techniques. On the other hand, several techniques focused on the first-order feature extraction of the chest X-Ray thus making the employed models less accurate and robust. This study presents Dual_Pachi: Attention Based Dual Path Framework with Intermediate Second Order-Pooling for more accurate and robust Chest X-ray feature extraction for Covid-19 detection. Dual_Pachi consists of 4 main building Blocks; Block one converts the received chest X-Ray image to CIE LAB coordinates (L & AB channels which are separated at the first three layers of a modified Inception V3 Architecture.). Block two further exploit the global features extracted from block one via a global second-order pooling while block three focuses on the low-level visual information and the high-level semantics of Chest X-ray image features using a multi-head self-attention and an MLP Layer without sacrificing performance. Finally, the fourth block is the classification block where classification is done using fully connected layers and SoftMax activation. Dual_Pachi is designed and trained in an end-to-end manner. According to the results, Dual_Pachi outperforms traditional deep learning models and other state-of-the-art approaches described in the literature with an accuracy of 0.96656 (Data_A) and 0.97867 (Data_B) for the Dual_Pachi approach and an accuracy of 0.95987 (Data_A) and 0.968 (Data_B) for the Dual_Pachi without attention block model. A Grad-CAM-based visualization is also built to highlight where the applied attention mechanism is concentrated.

COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network

The reliable and rapid identification of the COVID-19 has become crucial to prevent the rapid spread of the disease, ease lockdown restrictions and reduce pressure on public health infrastructures. Recently, several methods and techniques have been proposed to detect the SARS-CoV-2 virus using different images and data. However, this is the first study that will explore the possibility of using deep convolutional neural network (CNN) models to detect COVID-19 from electrocardiogram (ECG) trace images. In this work, COVID-19 and other cardiovascular diseases (CVDs) were detected using deep-learning techniques. A public dataset of ECG images consisting of 1937 images from five distinct categories, such as normal, COVID-19, myocardial infarction (MI), abnormal heartbeat (AHB), and recovered myocardial infarction (RMI) were used in this study. Six different deep CNN models (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and MobileNetv2) were used to investigate three different classification schemes: (i) two-class classification (normal vs COVID-19); (ii) three-class classification (normal, COVID-19, and other CVDs), and finally, (iii) five-class classification (normal, COVID-19, MI, AHB, and RMI). For two-class and three-class classification, Densenet201 outperforms other networks with an accuracy of 99.1%, and 97.36%, respectively; while for the five-class classification, InceptionV3 outperforms others with an accuracy of 97.83%. ScoreCAM visualization confirms that the networks are learning from the relevant area of the trace images. Since the proposed method uses ECG trace images which can be captured by smartphones and are readily available facilities in low-resources countries, this study will help in faster computer-aided diagnosis of COVID-19 and other cardiac abnormalities.

COVID-DSNet: A novel deep convolutional neural network for detection of coronavirus (SARS-CoV-2) cases from CT and Chest X-Ray images

COVID-19 (SARS-CoV-2), which causes acute respiratory syndrome, is a contagious and deadly disease that has devastating effects on society and human life. COVID-19 can cause serious complications, especially in patients with pre-existing chronic health problems such as diabetes, hypertension, lung cancer, weakened immune systems, and the elderly. The most critical step in the fight against COVID-19 is the rapid diagnosis of infected patients. Computed Tomography (CT), chest X-ray (CXR), and RT-PCR diagnostic kits are frequently used to diagnose the disease. However, due to difficulties such as the inadequacy of RT-PCR test kits and false negative (FN) results in the early stages of the disease, the time-consuming examination of medical images obtained from CT and CXR imaging techniques by specialists/doctors, and the increasing workload on specialists, it is challenging to detect COVID-19. Therefore, researchers have suggested searching for new methods in COVID- 19 detection. In analysis studies with CT and CXR radiography images, it was determined that COVID-19-infected patients experienced abnormalities related to COVID-19. The anomalies observed here are the primary motivation for artificial intelligence researchers to develop COVID-19 detection applications with deep convolutional neural networks. Here, convolutional neural network-based deep learning algorithms from artificial intelligence technologies with high discrimination capabilities can be considered as an alternative approach in the disease detection process. This study proposes a deep convolutional neural network, COVID-DSNet, to diagnose typical pneumonia (bacterial, viral) and COVID-19 diseases from CT, CXR, hybrid CT + CXR images. In the multi-classification study with the CT dataset, 97.60 % accuracy and 97.60 % sensitivity values were obtained from the COVID-DSNet model, and 100 %, 96.30 %, and 96.58 % sensitivity values were obtained in the detection of typical, common pneumonia and COVID-19, respectively. The proposed model is an economical, practical deep learning network that data scientists can benefit from and develop. Although it is not a definitive solution in disease diagnosis, it may help experts as it produces successful results in detecting pneumonia and COVID-19.

A novel approach for detection of COVID-19 and Pneumonia using only binary classification from chest CT-scans

The novel Coronavirus, Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) spread all over the world, causing a dramatic shift in circumstances that resulted in a massive pandemic, affecting the world's well-being and stability. It is an RNA virus that can infect both humans as well as animals. Diagnosis of the virus as soon as possible could contain and avoid a serious COVID-19 outbreak. Current pharmaceutical techniques and diagnostic methods tests such as Reverse Transcription-Polymerase Chain Reaction (RT-PCR) and Serology tests are time-consuming, expensive, and require a well-equipped laboratory for analysis, making them restrictive and inaccessible to everyone. Deep Learning has grown in popularity in recent years, and it now plays a crucial role in Image Classification, which also involves Medical Imaging. Using chest CT scans, this study explores the problem statement automation of differentiating COVID-19 contaminated individuals from healthy individuals. Convolutional Neural Networks (CNNs) can be trained to detect patterns in computed tomography scans (CT scans). Hence, different CNN models were used in the current study to identify variations in chest CT scans, with accuracies ranging from 91% to 98%. The Multiclass Classification method is used to build these architectures. This study also proposes a new approach for classifying CT images that use two binary classifications combined to work together, achieving 98.38% accuracy. All of these architectures' performances are compared using different classification metrics.

Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision

In the context of global pandemic Coronavirus disease 2019 (COVID-19) that threatens life of all human beings, it is of vital importance to achieve early detection of COVID-19 among symptomatic patients. In this paper, a computer aided diagnosis (CAD) model Cov-Net is proposed for accurate recognition of COVID-19 from chest X-ray images via machine vision techniques, which mainly concentrates on powerful and robust feature learning ability. In particular, a modified residual network with asymmetric convolution and attention mechanism embedded is selected as the backbone of feature extractor, after which skip-connected dilated convolution with varying dilation rates is applied to achieve sufficient feature fusion among high-level semantic and low-level detailed information. Experimental results on two public COVID-19 radiography databases have demonstrated the practicality of proposed Cov-Net in accurate COVID-19 recognition with accuracy of 0.9966 and 0.9901, respectively. Furthermore, within same experimental conditions, proposed Cov-Net outperforms other six state-of-the-art computer vision algorithms, which validates the superiority and competitiveness of Cov-Net in building highly discriminative features from the perspective of methodology. Hence, it is deemed that proposed Cov-Net has a good generalization ability so that it can be applied to other CAD scenarios. Consequently, one can conclude that this work has both practical value in providing reliable reference to the radiologist and theoretical significance in developing methods to build robust features with strong presentation ability.

Cloud-based COVID-19 disease prediction system from X-Ray images using convolutional neural network on smartphone

COVID-19 has engulfed over 200 nations through human-to-human transmission, either directly or indirectly. Reverse Transcription-polymerase Chain Reaction (RT-PCR) has been endorsed as a standard COVID-19 diagnostic procedure but has caveats such as low sensitivity, the need for a skilled workforce, and is time-consuming. Coronaviruses show significant manifestation in Chest X-Ray (CX-Ray) images and, thus, can be a viable option for an alternate COVID-19 diagnostic strategy. An automatic COVID-19 detection system can be developed to detect the disease, thus reducing strain on the healthcare system. This paper discusses a real-time Convolutional Neural Network (CNN) based system for COVID-19 illness prediction from CX-Ray images on the cloud. The implemented CNN model displays exemplary results, with training accuracy being 99.94% and validation accuracy reaching 98.81%. The confusion matrix was utilized to assess the models' outcome and achieved 99% precision, 98% recall, 99% F1 score, 100% training area under the curve (AUC) and 98.3% validation AUC. The same CX-Ray dataset was also employed to predict the COVID-19 disease with deep Convolution Neural Networks (DCNN), such as ResNet50, VGG19, InceptonV3, and Xception. The prediction outcome demonstrated that the present CNN was more capable than the DCNN models. The efficient CNN model was deployed to the Platform as a Service (PaaS) cloud.

Automated Lung-Related Pneumonia and COVID-19 Detection Based on Novel Feature Extraction Framework and Vision Transformer Approaches Using Chest X-ray Images

According to research, classifiers and detectors are less accurate when images are blurry, have low contrast, or have other flaws which raise questions about the machine learning model's ability to recognize items effectively. The chest X-ray image has proven to be the preferred image modality for medical imaging as it contains more information about a patient. Its interpretation is quite difficult, nevertheless. The goal of this research is to construct a reliable deep-learning model capable of producing high classification accuracy on chest x-ray images for lung diseases. To enable a thorough study of the chest X-ray image, the suggested framework first derived richer features using an ensemble technique, then a global second-order pooling is applied to further derive higher global features of the images. Furthermore, the images are then separated into patches and position embedding before analyzing the patches individually via a vision transformer approach. The proposed model yielded 96.01% sensitivity, 96.20% precision, and 98.00% accuracy for the COVID-19 Radiography Dataset while achieving 97.84% accuracy, 96.76% sensitivity and 96.80% precision, for the Covid-ChestX-ray-15k dataset. The experimental findings reveal that the presented models outperform traditional deep learning models and other state-of-the-art approaches provided in the literature.

CovidConvLSTM: A fuzzy ensemble model for COVID-19 detection from chest X-rays

The rapid outbreak of COVID-19 has affected the lives and livelihoods of a large part of the society. Hence, to confine the rapid spread of this virus, early detection of COVID-19 is extremely important. One of the most common ways of detecting COVID-19 is by using chest X-ray images. In the literature, it is found that most of the research activities applied convolutional neural network (CNN) models where the features generated by the last convolutional layer were directly passed to the classification models. In this paper, convolutional long short-term memory (ConvLSTM) layer is used in order to encode the spatial dependency among the feature maps obtained from the last convolutional layer of the CNN and to improve the image representational capability of the model. Additionally, the squeeze-and-excitation (SE) block, a spatial attention mechanism, is used to allocate weights to important local features. These two mechanisms are employed on three popular CNN models - VGG19, InceptionV3, and MobileNet to improve their classification strength. Finally, the Sugeno fuzzy integral based ensemble method is used on these classifiers' outputs to enhance the detection accuracy further. For experiments, three chest X-ray datasets, which are very prevalent for COVID-19 detection, are considered. For all the three datasets, it is found that the results obtained by the proposed method are comparable to state-of-the-art methods. The code, along with the pre-trained models, can be found at https://github.com/colabpro123/CovidConvLSTM.

Integration of cloud computing with the Internet of things for the treatment and management of the COVID-19 pandemic

The globe is now fighting the COVID-19 epidemic, and a cost-effective method to battle the outbreak is urgently required. In this era, the Internet of Things (IoT) offers healthcare systems or checks and discovers patients in epidemics to prevent the virus from spreading. In order to tackle the current COVID-19 pandemic, intelligent health care based on IoT is becoming increasingly vital in better digital technologies. This technology allows gadgets to link hospitals and other predefined sites to tackle this dilemma. The cognitive Internet of Medical Things (IoMT) is a potential technology for quick analysis, dynamic monitoring and control, improved therapy and management, and prevention of viral transmission. This technology can send a message to the hospital immediately in an emergency. Due to the importance of this subject, the influence of IoT-centered innovations in the management of COVID-19 is examined in this article. The three main areas, including early detection, viral prevention, and patient care, are considered in this paper. The results showed that using IoT, such as drones and robots, has been fruitful in reducing contact with patients and identifying disease symptoms. Also, they showed that the IoT wearable gadgets and the cloud platform to store the patient’s information have helped doctors track the patient’s condition.

Recommender System for the Efficient Treatment of COVID-19 Using a Convolutional Neural Network Model and Image Similarity

Background: Hospitals face a significant problem meeting patients' medical needs during epidemics, especially when the number of patients increases rapidly, as seen during the recent COVID-19 pandemic. This study designs a treatment recommender system (RS) for the efficient management of human capital and resources such as doctors, medicines, and resources in hospitals. We hypothesize that a deep learning framework, when combined with search paradigms in an image framework, can make the RS very efficient. Methodology: This study uses a Convolutional neural network (CNN) model for the feature extraction of the images and discovers the most similar patients. The input queries patients from the hospital database with similar chest X-ray images. It uses a similarity metric for the similarity computation of the images. Results: This methodology recommends the doctors, medicines, and resources associated with similar patients to a COVID-19 patients being admitted to the hospital. The performance of the proposed RS is verified with five different feature extraction CNN models and four similarity measures. The proposed RS with a ResNet-50 CNN feature extraction model and Maxwell-Boltzmann similarity is found to be a proper framework for treatment recommendation with a mean average precision of more than 0.90 for threshold similarities in the range of 0.7 to 0.9 and an average highest cosine similarity of more than 0.95. Conclusions: Overall, an RS with a CNN model and image similarity is proven as an efficient tool for the proper management of resources during the peak period of pandemics and can be adopted in clinical settings.

COVID-19 detection based on pre-trained deep networks and LSTM model using X-ray images enhanced contrast with artificial bee colony algorithm

Coronavirus (COVID-19) is an infectious disease that has spread across the world within a short period of time and is causing rapid casualties. The main symptoms of this virus are shortness of breath, fever, cough, and a sore throat. The virus is detected through samples, such as throat swabs and sputum, taken from people who meet the possible case definition and the results are usually obtained within a few hours or a day. The development of test kits to detect the COVID-19 virus is still an open research topic, and automated and faster diagnostic tools are needed. Recent studies have shown that biomedical images can be used for COVID-19 testing. This study proposes the hybrid use of pre-trained deep networks and the long short-term memory (LSTM) for the classification of COVID-19 from contrast-enhanced chest X-rays. In the proposed system, a transformation function is applied to X-ray images first. Then, the artificial bee colony (ABC) algorithm is used to optimize the parameters obtained from the transformation function. The pre-trained deep network models and LSTM are preferred to extract features from the contrast-enhanced chest X-rays. At the final stage, COVID-19, normal (healthy), and pneumonia chest X-ray are classified using softmax. To evaluate the performance of the proposed method, the "COVID-19 radiography" dataset, which is widely used in the literature, is preferred. From the proposed model, 98.97% accuracy, 98.80% precision, and 98.70% sensitivity rates are obtained. Experimental results reveal that the proposed model provides efficient results compared to other methods. Thanks to the application of ABC-based image enhancement, increased classification of 2.5% has been achieved against other state-of-the-art models.

LCSB-inception: Reliable and effective light-chroma separated branches for Covid-19 detection from chest X-ray images

According to the World Health Organization, an estimate of more than five million infections and 355,000 deaths have been recorded worldwide since the emergence of the coronavirus disease (COVID-19). Various researchers have developed interesting and effective deep learning frameworks to tackle this disease. However, poor feature extraction from the Chest X-ray images and the high computational cost of the available models impose difficulties to an accurate and fast Covid-19 detection framework. Thus, the major purpose of this study is to offer an accurate and efficient approach for extracting COVID-19 features from chest X-rays that is also less computationally expensive than earlier research. To achieve the specified goal, we explored the Inception V3 deep artificial neural network. This study proposed LCSB-Inception; a two-path (L and AB channel) Inception V3 network along the first three convolutional layers. The RGB input image is first transformed to CIE LAB coordinates (L channel which is aimed at learning the textural and edge features of the Chest X-Ray and AB channel which is aimed at learning the color variations of the Chest X-ray images). The L achromatic channel and the AB channels filters are set to 50%L-50%AB. This method saves between one-third and one-half of the parameters in the divided branches. We further introduced a global second-order pooling at the last two convolutional blocks for more robust image feature extraction against the conventional max-pooling. The detection accuracy of the LCSB-Inception is further improved by employing the Contrast Limited Adaptive Histogram Equalization (CLAHE) image enhancement technique on the input image before feeding them to the network. The proposed LCSB-Inception network is experimented on using two loss functions (Categorically smooth loss and categorically Cross-entropy) and two learning rates whereas Accuracy, Precision, Sensitivity, Specificity F1-Score, and AUC Score were used for evaluation via the chestX-ray-15k (Data_1) and COVID-19 Radiography dataset (Data_2). The proposed models produced an acceptable outcome with an accuracy of 0.97867 (Data_1) and 0.98199 (Data_2) according to the experimental findings. In terms of COVID-19 identification, the suggested models outperform conventional deep learning models and other state-of-the-art techniques presented in the literature based on the results.

IEViT: An enhanced vision transformer architecture for chest X-ray image classification

BACKGROUND AND OBJECTIVE

Chest X-ray imaging is a relatively cheap and accessible diagnostic tool that can assist in the diagnosis of various conditions, including pneumonia, tuberculosis, COVID-19, and others. However, the requirement for expert radiologists to view and interpret chest X-ray images can be a bottleneck, especially in remote and deprived areas. Recent advances in machine learning have made possible the automated diagnosis of chest X-ray scans. In this work, we examine the use of a novel Transformer-based deep learning model for the task of chest X-ray image classification.

METHODS

We first examine the performance of the Vision Transformer (ViT) state-of-the-art image classification machine learning model for the task of chest X-ray image classification, and then propose and evaluate the Input Enhanced Vision Transformer (IEViT), a novel enhanced Vision Transformer model that can achieve improved performance on chest X-ray images associated with various pathologies.

RESULTS

Experiments on four chest X-ray image data sets containing various pathologies (tuberculosis, pneumonia, COVID-19) demonstrated that the proposed IEViT model outperformed ViT for all the data sets and variants examined, achieving an F1-score between 96.39% and 100%, and an improvement over ViT of up to +5.82% in terms of F1-score across the four examined data sets. IEViT's maximum sensitivity (recall) ranged between 93.50% and 100% across the four data sets, with an improvement over ViT of up to +3%, whereas IEViT's maximum precision ranged between 97.96% and 100% across the four data sets, with an improvement over ViT of up to +6.41%.

CONCLUSIONS

Results showed that the proposed IEViT model outperformed all ViT's variants for all the examined chest X-ray image data sets, demonstrating its superiority and generalisation ability. Given the relatively low cost and the widespread accessibility of chest X-ray imaging, the use of the proposed IEViT model can potentially offer a powerful, but relatively cheap and accessible method for assisting diagnosis using chest X-ray images.

COVID-19 infection prediction from CT scan images of lungs using Iterative Convolution Neural Network model

World Health Organization has defined COVID-19 as a contagious, communicable and fast spreading disease engendered by the Corona virus, SARS-CoV-2, is a respirational microorganism. Computerized Tomography (CT) scan images of the chest helps in detecting COVID 19 infection in a fast way with much reliability. In this paper, chest CT scan images of COVID and Non-COVID categories are considered to train the supervised classifier, Iterative convolution Neural Network. The training process is done with six different training data size. The trained models are iterated for the fixed size of testing data (20 images). The same set of training and testing processes are done with two different Iterative Convolutional Neural Network architectures, one with two hidden layers (CNN1) and another with three hidden layers (CNN2). The iterations are extended up to 7, but the model performance is degraded after the 6th iteration, which makes to fix the iteration level as 5 for both CNN models. Six different training sets with five iterations have led into 30 CNN models. For two different CNN architectures, which lead to 60 different models. The model designed with 100 training sets in both CNN1 and CNN2, have produced the high accuracy in COVID classification than any other models. The better classification accuracy 89% is achieved from CNN2 model with its 5th iteration.

A self-supervised COVID-19 CT recognition system with multiple regularizations

The diagnosis of Coronavirus Disease 2019 (COVID-19) exploiting machine learning algorithms based on chest computed tomography (CT) images has become an important technology. Though many excellent computer-aided methods leveraging CT images have been designed, they do not possess sufficiently high recognition accuracy. Besides, these methods entail vast amounts of training data, which might be difficult to be satisfied in some real-world applications. To address these two issues, this paper proposes a novel COVID-19 recognition system based on CT images, which has high recognition accuracy, while only requiring a small amount of training data. Specifically, the system possesses the following three improvements: 1) Data: a novel redesigned BCELoss that incorporates Label Smoothing, Focal Loss, and Label Weighting Regularization (LSFLLW-R) technique for optimizing the solution space and preventing overfitting, 2) Model: a backbone network processed by two-phase contrastive self-supervised learning for classifying multiple labels, and 3) Method: a decision-fusing ensemble learning method for getting a more stable system, with balanced metric values. Our proposed system is evaluated on the small-scale expanded COVID-CT dataset, achieving an accuracy of 94.3%, a precision of 94.1%, a recall (sensitivity) of 93.4%, an F1-score of 94.7%, and an Area Under the Curve (AUC) of 98.9%, for COVID-19 diagnosis, respectively. These experimental results verify that our system can not only identify pathological locations effectively, but also achieve better performance in terms of accuracy, generalizability, and stability, compared with several other state-of-the-art COVID-19 diagnosis methods.

Custom Hardware Architectures for Deep Learning on Portable Devices: A Review

The staggering innovations and emergence of numerous deep learning (DL) applications have forced researchers to reconsider hardware architecture to accommodate fast and efficient application-specific computations. Applications, such as object detection, image recognition, speech translation, as well as music synthesis and image generation, can be performed with high accuracy at the expense of substantial computational resources using DL. Furthermore, the desire to adopt Industry 4.0 and smart technologies within the Internet of Things infrastructure has initiated several studies to enable on-chip DL capabilities for resource-constrained devices. Specialized DL processors reduce dependence on cloud servers, improve privacy, lessen latency, and mitigate bandwidth congestion. As we reach the limits of shrinking transistors, researchers are exploring various application-specific hardware architectures to meet the performance and efficiency requirements for DL tasks. Over the past few years, several software optimizations and hardware innovations have been proposed to efficiently perform these computations. In this article, we review several DL accelerators, as well as technologies with emerging devices, to highlight their architectural features in application-specific integrated circuit (IC) and field-programmable gate array (FPGA) platforms. Finally, the design considerations for DL hardware in portable applications have been discussed, along with some deductions about the future trends and potential research directions to innovate DL accelerator architectures further. By compiling this review, we expect to help aspiring researchers widen their knowledge in custom hardware architectures for DL.

SVD-CLAHE boosting and balanced loss function for Covid-19 detection from an imbalanced Chest X-Ray dataset

Covid-19 disease has had a disastrous effect on the health of the global population, for the last two years. Automatic early detection of Covid-19 disease from Chest X-Ray (CXR) images is a very crucial step for human survival against Covid-19. In this paper, we propose a novel data-augmentation technique, called SVD-CLAHE Boosting and a novel loss function Balanced Weighted Categorical Cross Entropy (BWCCE), in order to detect Covid 19 disease efficiently from a highly class-imbalanced Chest X-Ray image dataset. Our proposed SVD-CLAHE Boosting method is comprised of both oversampling and under-sampling methods. First, a novel Singular Value Decomposition (SVD) based contrast enhancement and Contrast Limited Adaptive Histogram Equalization (CLAHE) methods are employed for oversampling the data in minor classes. Simultaneously, a Random Under Sampling (RUS) method is incorporated in major classes, so that the number of images per class will be more balanced. Thereafter, Balanced Weighted Categorical Cross Entropy (BWCCE) loss function is proposed in order to further reduce small class imbalance after SVD-CLAHE Boosting. Experimental results reveal that ResNet-50 model on the augmented dataset (by SVD-CLAHE Boosting), along with BWCCE loss function, achieved 95% F1 score, 94% accuracy, 95% recall, 96% precision and 96% AUC, which is far better than the results by other conventional Convolutional Neural Network (CNN) models like InceptionV3, DenseNet-121, Xception etc. as well as other existing models like Covid-Lite and Covid-Net. Hence, our proposed framework outperforms other existing methods for Covid-19 detection. Furthermore, the same experiment is conducted on VGG-19 model in order to check the validity of our proposed framework. Both ResNet-50 and VGG-19 model are pre-trained on the ImageNet dataset. We publicly shared our proposed augmented dataset on Kaggle website (https://www.kaggle.com/tr1gg3rtrash/balanced-augmented-covid-cxr-dataset), so that any research community can widely utilize this dataset. Our code is available on GitHub website online (https://github.com/MrinalTyagi/SVD-CLAHE-and-BWCCE).