Convolutional Sparse Support Estimator-Based COVID-19 Recognition From X-Ray Images

Performance evaluation of reduced complexity deep neural networks

Deep Neural Networks (DNN) have achieved state-of-the-art performance in medical image classification and are increasingly being used for disease diagnosis. However, these models are quite complex and that necessitates the need to reduce the model complexity for their use in low-power edge applications that are becoming common. The model complexity reduction techniques in most cases comprise of time-consuming operations and are often associated with a loss of model performance in proportion to the model size reduction. In this paper, we propose a simplified model complexity reduction technique based on reducing the number of channels for any DNN and demonstrate the complexity reduction approaches for the ResNet-50 model integration in low-power devices. The model performance of the proposed models was evaluated for multiclass classification of CXR images, as normal, pneumonia, and COVID-19 classes. We demonstrate successive size reductions down to 75%, 87%, and 93% reduction with an acceptable classification performance reduction of 0.5%, 0.5%, and 0.8% respectively. We also provide the results for the model generalization, and visualization with Grad-CAM at an acceptable performance and interpretable level. In addition, a theoretical VLSI architecture for the best performing architecture has been presented.

Lightweight convolutional neural network for chest X-ray images classification

In this study, we developed a lightweight and rapid convolutional neural network (CNN) architecture for chest X-ray images; it primarily consists of a redesigned feature extraction (FE) module and multiscale feature (MF) module and validated using publicly available COVID-19 datasets. Experiments were conducted on multiple updated versions of the COVID-19 Radiography Database, a publicly accessible dataset on Kaggle. The database contained images categorized into three classes: COVID-19 coronavirus, viral or bacterial pneumonia, and normal. The results revealed that the proposed method achieved a training accuracy of 99.85% and a validation accuracy of 96.28% when detecting the three classes. In the test set, the optimal results were 96.03% accuracy for COVID-19, 97.10% accuracy for viral or bacterial pneumonia, and 97.86% accuracy for normal individuals. By reducing the computational requirements and improving the speed of the model, the proposed method can achieve real-time, low-error performance to help medical professionals with rapid diagnosis of COVID-19.

A Collaborative Multimodal Learning-Based Framework for COVID-19 Diagnosis

The pandemic of coronavirus disease 2019 (COVID-19) has led to a global public health crisis, which caused millions of deaths and billions of infections, greatly increasing the pressure on medical resources. With the continuous emergence of viral mutations, developing automated tools for COVID-19 diagnosis is highly desired to assist the clinical diagnosis and reduce the tedious workload of image interpretation. However, medical images in a single site are usually of a limited amount or weakly labeled, while integrating data scattered around different institutions to build effective models is not allowed due to data policy restrictions. In this article, we propose a novel privacy-preserving cross-site framework for COVID-19 diagnosis with multimodal data, seeking to effectively leverage heterogeneous data from multiple parties while preserving patients' privacy. Specifically, a Siamese branched network is introduced as the backbone to capture inherent relationships across heterogeneous samples. The redesigned network is capable of handling semisupervised inputs in multimodalities and conducting task-specific training, in order to improve the model performance of various scenarios. The framework achieves significant improvement compared with state-of-the-art methods, as we demonstrate through extensive simulations on real-world datasets.

Multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation from CT scans

Accurate segmentation of COVID-19 lesions from medical images is essential for achieving precise diagnosis and developing effective treatment strategies. Unfortunately, this task presents significant challenges, owing to the complex and diverse characteristics of opaque areas, subtle differences between infected and healthy tissue, and the presence of noise in CT images. To address these difficulties, this paper designs a new deep-learning architecture (named MD-Net) based on multi-scale input layers and dense decoder aggregation network for COVID-19 lesion segmentation. In our framework, the U-shaped structure serves as the cornerstone to facilitate complex hierarchical representations essential for accurate segmentation. Then, by introducing the multi-scale input layers (MIL), the network can effectively analyze both fine-grained details and contextual information in the original image. Furthermore, we introduce an SE-Conv module in the encoder network, which can enhance the ability to identify relevant information while simultaneously suppressing the transmission of extraneous or non-lesion information. Additionally, we design a dense decoder aggregation (DDA) module to integrate feature distributions and important COVID-19 lesion information from adjacent encoder layers. Finally, we conducted a comprehensive quantitative analysis and comparison between two publicly available datasets, namely Vid-QU-EX and QaTa-COV19-v2, to assess the robustness and versatility of MD-Net in segmenting COVID-19 lesions. The experimental results show that the proposed MD-Net has superior performance compared to its competitors, and it exhibits higher scores on the Dice value, Matthews correlation coefficient (Mcc), and Jaccard index. In addition, we also conducted ablation studies on the Vid-QU-EX dataset to evaluate the contributions of each key component within the proposed architecture.

A Distance Transformation Deep Forest Framework With Hybrid-Feature Fusion for CXR Image Classification

Detecting pneumonia, especially coronavirus disease 2019 (COVID-19), from chest X-ray (CXR) images is one of the most effective ways for disease diagnosis and patient triage. The application of deep neural networks (DNNs) for CXR image classification is limited due to the small sample size of the well-curated data. To tackle this problem, this article proposes a distance transformation-based deep forest framework with hybrid-feature fusion (DTDF-HFF) for accurate CXR image classification. In our proposed method, hybrid features of CXR images are extracted in two ways: hand-crafted feature extraction and multigrained scanning. Different types of features are fed into different classifiers in the same layer of the deep forest (DF), and the prediction vector obtained at each layer is transformed to form distance vector based on a self-adaptive scheme. The distance vectors obtained by different classifiers are fused and concatenated with the original features, then input into the corresponding classifier at the next layer. The cascade grows until DTDF-HFF can no longer gain benefits from the new layer. We compare the proposed method with other methods on the public CXR datasets, and the experimental results show that the proposed method can achieve state-of-the art (SOTA) performance. The code will be made publicly available at https://github.com/hongqq/DTDF-HFF.

MiniSeg: An Extremely Minimum Network Based on Lightweight Multiscale Learning for Efficient COVID-19 Segmentation

The rapid spread of the new pandemic, i.e., coronavirus disease 2019 (COVID-19), has severely threatened global health. Deep-learning-based computer-aided screening, e.g., COVID-19 infected area segmentation from computed tomography (CT) image, has attracted much attention by serving as an adjunct to increase the accuracy of COVID-19 screening and clinical diagnosis. Although lesion segmentation is a hot topic, traditional deep learning methods are usually data-hungry with millions of parameters, easy to overfit under limited available COVID-19 training data. On the other hand, fast training/testing and low computational cost are also necessary for quick deployment and development of COVID-19 screening systems, but traditional methods are usually computationally intensive. To address the above two problems, we propose MiniSeg, a lightweight model for efficient COVID-19 segmentation from CT images. Our efforts start with the design of an attentive hierarchical spatial pyramid (AHSP) module for lightweight, efficient, effective multiscale learning that is essential for image segmentation. Then, we build a two-path (TP) encoder for deep feature extraction, where one path uses AHSP modules for learning multiscale contextual features and the other is a shallow convolutional path for capturing fine details. The two paths interact with each other for learning effective representations. Based on the extracted features, a simple decoder is added for COVID-19 segmentation. For comparing MiniSeg to previous methods, we build a comprehensive COVID-19 segmentation benchmark. Extensive experiments demonstrate that the proposed MiniSeg achieves better accuracy because its only 83k parameters make it less prone to overfitting. Its high efficiency also makes it easy to deploy and develop. The code has been released at https://github.com/yun-liu/MiniSeg.

Multitask Adversarial Networks Based on Extensive Nonlinear Spiking Neuron Models

Deep learning technology has been successfully used in Chest X-ray (CXR) images of COVID-19 patients. However, due to the characteristics of COVID-19 pneumonia and X-ray imaging, the deep learning methods still face many challenges, such as lower imaging quality, fewer training samples, complex radiological features and irregular shapes. To address these challenges, this study first introduces an extensive NSNP-like neuron model, and then proposes a multitask adversarial network architecture based on ENSNP-like neurons for chest X-ray images of COVID-19, called MAE-Net. The MAE-Net serves two tasks: (i) converting low-quality CXR images to high-quality images; (ii) classifying CXR images of COVID-19. The adversarial architecture of MAE-Net uses two generators and two discriminators, and two new loss functions have been introduced to guide the optimization of the network. The MAE-Net is tested on four benchmark COVID-19 CXR image datasets and compared them with eight deep learning models. The experimental results show that the proposed MAE-Net can enhance the conversion quality and the accuracy of image classification results.

Dynamical Modeling and Qualitative Analysis of a Delayed Model for CD8 T Cells in Response to Viral Antigens

Although the immune effector CD8 T cells play a crucial role in clearance of viruses, the mechanisms underlying the dynamics of how CD8 T cells respond to viral infection remain largely unexplored. Here, we develop a delayed model that incorporates CD8 T cells and infected cells to investigate the functional role of CD8 T cells in persistent virus infection. Bifurcation analysis reveals that the model has four steady states that can finely divide the progressions of viral infection into four states, and endows the model with bistability that has ability to achieve the switch from one state to another. Furthermore, analytical and numerical methods find that the time delay resulting from incubation period of virus can induce a stable low-infection steady state to be oscillatory, coexisting with a stable high-infection steady state in phase space. In particular, a novel mechanism to achieve the switch between two stable steady states, time-delay-based switch, is proposed, where the initial conditions and other parameters of the model remain unchanged. Moreover, our model predicts that, for a certain range of initial antigen load: 1) under a longer incubation period, the lower the initial antigen load, the easier the virus infection will evolve into severe state; while the higher the initial antigen load, the easier it is for the virus infection to be effectively controlled and 2) only when the incubation period is small, the lower the initial antigen load, the easier it is to effectively control the infection progression. Our results are consistent with multiple experimental observations, which may facilitate the understanding of the dynamical and physiological mechanisms of CD8 T cells in response to viral infections.

Observer-Based Neural Control of N-Link Flexible-Joint Robots

This article concentrates on the adaptive neural control approach of n -link flexible-joint electrically driven robots. The presented control method only needs to know the position and armature current information of the flexible-joint manipulator. An adaptive observer is designed to estimate the velocities of links and motors, and radial basis function neural networks are applied to approximate the unknown nonlinearities. Based on the backstepping technique and the Lyapunov stability theory, the observer-based neural control issue is addressed by relying on uplink-event-triggered states only. It is demonstrated that all signals are semi-globally ultimately uniformly bounded and the tracking errors can converge to a small neighborhood of zero. Finally, simulation results are shown to validate the designed event-triggered control strategy.

A Deep Ensemble Dynamic Learning Network for Corona Virus Disease 2019 Diagnosis

Corona virus disease 2019 is an extremely fatal pandemic around the world. Intelligently recognizing X-ray chest radiography images for automatically identifying corona virus disease 2019 from other types of pneumonia and normal cases provides clinicians with tremendous conveniences in diagnosis process. In this article, a deep ensemble dynamic learning network is proposed. After a chain of image preprocessing steps and the division of image dataset, convolution blocks and the final average pooling layer are pretrained as a feature extractor. For classifying the extracted feature samples, two-stage bagging dynamic learning network is trained based on neural dynamic learning and bagging algorithms, which diagnoses the presence and types of pneumonia successively. Experimental results manifest that using the proposed deep ensemble dynamic learning network obtains 98.7179% diagnosis accuracy, which indicates more excellent diagnosis effect than existing state-of-the-art models on the open image dataset. Such accurate diagnosis effects provide convincing evidences for further detections and treatments.

Artificial intelligence-assisted diagnosis of congenital heart disease and associated pulmonary arterial hypertension from chest radiographs: A multi-reader multi-case study

OBJECTIVES

To explore the possibility of automatic diagnosis of congenital heart disease (CHD) and pulmonary arterial hypertension associated with CHD (PAH-CHD) from chest radiographs using artificial intelligence (AI) technology and to evaluate whether AI assistance could improve clinical diagnostic accuracy.

MATERIALS AND METHODS

A total of 3255 frontal preoperative chest radiographs (1174 CHD of any type and 2081 non-CHD) were retrospectively obtained. In this study, we adopted ResNet18 pretrained with the ImageNet database to establish diagnostic models. Radiologists diagnosed CHD/PAH-CHD from 330/165 chest radiographs twice: the first time, 50% of the images were accompanied by AI-based classification; after a month, the remaining 50% were accompanied by AI-based classification. Diagnostic results were compared between the radiologists and AI models, and between radiologists with and without AI assistance.

RESULTS

The AI model achieved an average area under the receiver operating characteristic curve (AUC) of 0.948 (sensitivity: 0.970, specificity: 0.982) for CHD diagnoses and an AUC of 0.778 (sensitivity: 0.632, specificity: 0.925) for identifying PAH-CHD. In the 330 balanced (165 CHD and 165 non-CHD) testing set, AI achieved higher AUCs than all 5 radiologists in the identification of CHD (0.670-0.858) and PAH-CHD (0.610-0.688). With AI assistance, the mean ± standard error AUC of radiologists was significantly improved for CHD (ΔAUC + 0.096, 95 % CI: 0.001-0.190; P = 0.048) and PAH-CHD (ΔAUC + 0.066, 95 % CI: 0.010-0.122; P = 0.031) diagnosis.

CONCLUSION

Chest radiograph-based AI models can detect CHD and PAH-CHD automatically. AI assistance improved radiologists' diagnostic accuracy, which may facilitate a timely initial diagnosis of CHD and PAH-CHD.

AFEX-Net: Adaptive feature extraction convolutional neural network for classifying computerized tomography images

Purpose

Deep convolutional neural networks are favored methods that are widely used in medical image processing due to their demonstrated performance in this area. Recently, the emergence of new lung diseases, such as COVID-19, and the possibility of early detection of their symptoms from chest computerized tomography images has attracted many researchers to classify diseases by training deep convolutional neural networks on lung computerized tomography images. The trained networks are expected to distinguish between different lung indications in various diseases, especially at the early stages. The purpose of this study is to introduce and assess an efficient deep convolutional neural network, called AFEX-Net, that can classify different lung diseases from chest computerized tomography images.

Methods

We designed a lightweight convolutional neural network called AFEX-Net with adaptive feature extraction layers, adaptive pooling layers, and adaptive activation functions. We trained and tested AFEX-Net on a dataset of more than 10,000 chest computerized tomography slices from different lung diseases (CC dataset), using an effective pre-processing method to remove bias. We also applied AFEX-Net to the public COVID-CTset dataset to assess its generalizability. The study was mainly conducted based on data collected over approximately six months during the pandemic outbreak in Afzalipour Hospital, Iran, which is the largest hospital in Southeast Iran.

Results

AFEX-Net achieved high accuracy and fast training on both datasets, outperforming several state-of-the-art convolutional neural networks. It has an accuracy of 99.7 % and 98.8 % on the CC and COVID-CTset datasets, respectively, with a learning speed that is 3 times faster compared to similar methods due to its lightweight structure. AFEX-Net was able to extract distinguishing features and classify chest computerized tomography images, especially at the early stages of lung diseases.

Conclusion

The AFEX-Net is a high-performing convolutional neural network for classifying lung diseases from chest CT images. It is efficient, adaptable, and compatible with input data, making it a reliable tool for early detection and diagnosis of lung diseases.

How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images

Over the past decade, the use of deep learning has been widely increasing in the medical image diagnosis field. Deep learning-based methods' (DLMs) performance strongly relies on training data. Therefore, researchers often focus on collecting as much data as possible from different medical facilities or developing approaches to avoid the impact of inter-category imbalance (ICI), which means a difference in data quantity among categories. However, due to the ICI within each medical facility, medical data are often isolated and acquired in different settings among medical facilities, known as the issue of intra-source imbalance (ISI) characteristic. This imbalance also impacts the performance of DLMs but receives negligible attention. In this study, we study the impact of the ISI on DLMs by comparison of the version of a deep learning model that was trained separately by an intra-source imbalanced chest X-ray (CXR) dataset and an intra-source balanced CXR dataset for COVID-19 diagnosis. The finding is that using the intra-source imbalanced dataset causes a serious training bias, although the dataset has a good inter-category balance. In contrast, the deep learning model performed a reliable diagnosis when trained on the intra-source balanced dataset. Therefore, our study reports clear evidence that the intra-source balance is vital for training data to minimize the risk of poor performance of DLMs.

Parallel CNN-ELM: A multiclass classification of chest X-ray images to identify seventeen lung diseases including COVID-19

Numerous epidemic lung diseases such as COVID-19, tuberculosis (TB), and pneumonia have spread over the world, killing millions of people. Medical specialists have experienced challenges in correctly identifying these diseases due to their subtle differences in Chest X-ray images (CXR). To assist the medical experts, this study proposed a computer-aided lung illness identification method based on the CXR images. For the first time, 17 different forms of lung disorders were considered and the study was divided into six trials with each containing two, two, three, four, fourteen, and seventeen different forms of lung disorders. The proposed framework combined robust feature extraction capabilities of a lightweight parallel convolutional neural network (CNN) with the classification abilities of the extreme learning machine algorithm named CNN-ELM. An optimistic accuracy of 90.92% and an area under the curve (AUC) of 96.93% was achieved when 17 classes were classified side by side. It also accurately identified COVID-19 and TB with 99.37% and 99.98% accuracy, respectively, in 0.996 microseconds for a single image. Additionally, the current results also demonstrated that the framework could outperform the existing state-of-the-art (SOTA) models. On top of that, a secondary conclusion drawn from this study was that the prospective framework retained its effectiveness over a range of real-world environments, including balanced-unbalanced or large-small datasets, large multiclass or simple binary class, and high- or low-resolution images. A prototype Android App was also developed to establish the potential of the framework in real-life implementation.

Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome

Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or 'equivocal' images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the 'equivocal' class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems.

Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture

Around the world, several lung diseases such as pneumonia, cardiomegaly, and tuberculosis (TB) contribute to severe illness, hospitalization or even death, particularly for elderly and medically vulnerable patients. In the last few decades, several new types of lung-related diseases have taken the lives of millions of people, and COVID-19 has taken almost 6.27 million lives. To fight against lung diseases, timely and correct diagnosis with appropriate treatment is crucial in the current COVID-19 pandemic. In this study, an intelligent recognition system for seven lung diseases has been proposed based on machine learning (ML) techniques to aid the medical experts. Chest X-ray (CXR) images of lung diseases were collected from several publicly available databases. A lightweight convolutional neural network (CNN) has been used to extract characteristic features from the raw pixel values of the CXR images. The best feature subset has been identified using the Pearson Correlation Coefficient (PCC). Finally, the extreme learning machine (ELM) has been used to perform the classification task to assist faster learning and reduced computational complexity. The proposed CNN-PCC-ELM model achieved an accuracy of 96.22% with an Area Under Curve (AUC) of 99.48% for eight class classification. The outcomes from the proposed model demonstrated better performance than the existing state-of-the-art (SOTA) models in the case of COVID-19, pneumonia, and tuberculosis detection in both binary and multiclass classifications. For eight class classification, the proposed model achieved precision, recall and fi-score and ROC are 100%, 99%, 100% and 99.99% respectively for COVID-19 detection demonstrating its robustness. Therefore, the proposed model has overshadowed the existing pioneering models to accurately differentiate COVID-19 from the other lung diseases that can assist the medical physicians in treating the patient effectively.

PCovNet+: A CNN-VAE anomaly detection framework with LSTM embeddings for smartwatch-based COVID-19 detection

The world is slowly recovering from the Coronavirus disease 2019 (COVID-19) pandemic; however, humanity has experienced one of its According to work by Mishra et al. (2020), the study's first phase included a cohort of 5,262 subjects, with 3,325 Fitbit users constituting the majority. However, among this large cohort of 5,262 subjects, most significant trials in modern times only to learn about its lack of preparedness in the face of a highly contagious pathogen. To better prepare the world for any new mutation of the same pathogen or the newer ones, technological development in the healthcare system is a must. Hence, in this work, PCovNet+, a deep learning framework, was proposed for smartwatches and fitness trackers to monitor the user's Resting Heart Rate (RHR) for the infection-induced anomaly. A convolutional neural network (CNN)-based variational autoencoder (VAE) architecture was used as the primary model along with a long short-term memory (LSTM) network to create latent space embeddings for the VAE. Moreover, the framework employed pre-training using normal data from healthy subjects to circumvent the data shortage problem in the personalized models. This framework was validated on a dataset of 68 COVID-19-infected subjects, resulting in anomalous RHR detection with precision, recall, F-beta, and F-1 score of 0.993, 0.534, 0.9849, and 0.6932, respectively, which is a significant improvement compared to the literature. Furthermore, the PCovNet+ framework successfully detected COVID-19 infection for 74% of the subjects (47% presymptomatic and 27% post-symptomatic detection). The results prove the usability of such a system as a secondary diagnostic tool enabling continuous health monitoring and contact tracing.

Deep Learning Methods for Interpretation of Pulmonary CT and X-ray Images in Patients with COVID-19-Related Lung Involvement: A Systematic Review

SARS-CoV-2 is a novel virus that has been affecting the global population by spreading rapidly and causing severe complications, which require prompt and elaborate emergency treatment. Automatic tools to diagnose COVID-19 could potentially be an important and useful aid. Radiologists and clinicians could potentially rely on interpretable AI technologies to address the diagnosis and monitoring of COVID-19 patients. This paper aims to provide a comprehensive analysis of the state-of-the-art deep learning techniques for COVID-19 classification. The previous studies are methodically evaluated, and a summary of the proposed convolutional neural network (CNN)-based classification approaches is presented. The reviewed papers have presented a variety of CNN models and architectures that were developed to provide an accurate and quick automatic tool to diagnose the COVID-19 virus based on presented CT scan or X-ray images. In this systematic review, we focused on the critical components of the deep learning approach, such as network architecture, model complexity, parameter optimization, explainability, and dataset/code availability. The literature search yielded a large number of studies over the past period of the virus spread, and we summarized their past efforts. State-of-the-art CNN architectures, with their strengths and weaknesses, are discussed with respect to diverse technical and clinical evaluation metrics to safely implement current AI studies in medical practice.

Explanatory Interactive Machine Learning

The most promising standard machine learning methods can deliver highly accurate classification results, often outperforming standard white-box methods. However, it is hardly possible for humans to fully understand the rationale behind the black-box results, and thus, these powerful methods hamper the creation of new knowledge on the part of humans and the broader acceptance of this technology. Explainable Artificial Intelligence attempts to overcome this problem by making the results more interpretable, while Interactive Machine Learning integrates humans into the process of insight discovery. The paper builds on recent successes in combining these two cutting-edge technologies and proposes how Explanatory Interactive Machine Learning (XIL) is embedded in a generalizable Action Design Research (ADR) process – called XIL-ADR. This approach can be used to analyze data, inspect models, and iteratively improve them. The paper shows the application of this process using the diagnosis of viral pneumonia, e.g., Covid-19, as an illustrative example. By these means, the paper also illustrates how XIL-ADR can help identify shortcomings of standard machine learning projects, gain new insights on the part of the human user, and thereby can help to unlock the full potential of AI-based systems for organizations and research.

Automatic lung disease classification from the chest X-ray images using hybrid deep learning algorithm

The chest X-ray images provide vital information about the congestion cost-effectively. We propose a novel Hybrid Deep Learning Algorithm (HDLA) framework for automatic lung disease classification from chest X-ray images. The model consists of steps including pre-processing of chest X-ray images, automatic feature extraction, and detection. In a pre-processing step, our goal is to improve the quality of raw chest X-ray images using the combination of optimal filtering without data loss. The robust Convolutional Neural Network (CNN) is proposed using the pre-trained model for automatic lung feature extraction. We employed the 2D CNN model for the optimum feature extraction in minimum time and space requirements. The proposed 2D CNN model ensures robust feature learning with highly efficient 1D feature estimation from the input pre-processed image. As the extracted 1D features have suffered from significant scale variations, we optimized them using min-max scaling. We classify the CNN features using the different machine learning classifiers such as AdaBoost, Support Vector Machine (SVM), Random Forest (RM), Backpropagation Neural Network (BNN), and Deep Neural Network (DNN). The experimental results claim that the proposed model improves the overall accuracy by 3.1% and reduces the computational complexity by 16.91% compared to state-of-the-art methods.

Biases associated with database structure for COVID-19 detection in X-ray images

Several artificial intelligence algorithms have been developed for COVID-19-related topics. One that has been common is the COVID-19 diagnosis using chest X-rays, where the eagerness to obtain early results has triggered the construction of a series of datasets where bias management has not been thorough from the point of view of patient information, capture conditions, class imbalance, and careless mixtures of multiple datasets. This paper analyses 19 datasets of COVID-19 chest X-ray images, identifying potential biases. Moreover, computational experiments were conducted using one of the most popular datasets in this domain, which obtains a 96.19% of classification accuracy on the complete dataset. Nevertheless, when evaluated with the ethical tool Aequitas, it fails on all the metrics. Ethical tools enhanced with some distribution and image quality considerations are the keys to developing or choosing a dataset with fewer bias issues. We aim to provide broad research on dataset problems, tools, and suggestions for future dataset developments and COVID-19 applications using chest X-ray images.

Representation based regression for object distance estimation

In this study, we propose a novel approach to predict the distances of the detected objects in an observed scene. The proposed approach modifies the recently proposed Convolutional Support Estimator Networks (CSENs). CSENs are designed to compute a direct mapping for the Support Estimation (SE) task in a representation-based classification problem. We further propose and demonstrate that representation-based methods (sparse or collaborative representation) can be used in well-designed regression problems especially over scarce data. To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR). The initial version of CSENs has a proxy mapping stage (i.e., a coarse estimation for the support set) that is required for the input. In this study, we improve the CSEN model by proposing Compressive Learning CSEN (CL-CSEN) that has the ability to jointly optimize the so-called proxy mapping stage along with convolutional layers. The experimental evaluations using the KITTI 3D Object Detection distance estimation dataset show that the proposed method can achieve a significantly improved distance estimation performance over all competing methods. Finally, the software implementations of the methods are publicly shared at https://github.com/meteahishali/CSENDistance.

Random forest model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression model

OBJECTIVE

To explore if random forest (RF) model can predict the prognosis of hospital-acquired Klebsiella pneumoniae infection as well as traditional logistic regression(LR) model.

METHODS

A total of 254 cases of hospital-acquired Klebsiella pneumoniae infection in a tertiary hospital in Beijing from January 2016 to December 2020 were retrospectively collected. Appropriate influencing factors were selected by referring to relevant articles from the aspects of basic clinical information and contact history before infection, and divided into a training set and a test set. Both the RF and LR models were trained by the training set, and using testing set to compare these two models.

RESULTS

The prediction accuracy of the LR model was 87.0%, the true positive rate of the LR model was 94.7%; the false negative rate of the LR model was 5.3%; the false positive rate of the LR model was 35%; the true negative rate of the LR model was 65%; the sensitivity of the LR model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 94.7%; and the specificity was 65%. The prediction accuracy of the RF model was 89.6%; the true positive rate of the RF model was 92.1%; the false negative rate of the RF model was 7.9%; the false positive rate of the RF model was 21.4%; the true negative rate of the RF model was 78.6%; the sensitivity of the RF model for the prognosis prediction of hospital-acquired Klebsiella pneumoniae infection was 92.1%; and the specificity was 78.6%. ROC curve shows that the area under curve(AUC) of the LR model was 0.91, and that of the RF model was 0.95.

CONCLUSION

The RF model has higher specificity, sensitivity, and accuracy for the prognostic prediction of hospital-acquired Klebsiella pneumoniae infection than the LR model and has greater clinical application prospects.

Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review

Lung, being one of the most important organs in human body, is often affected by various SARS diseases, among which COVID-19 has been found to be the most fatal disease in recent times. In fact, SARS-COVID 19 led to pandemic that spreads fast among the community causing respiratory problems. Under such situation, radiological imaging-based screening [mostly chest X-ray and computer tomography (CT) modalities] has been performed for rapid screening of the disease as it is a non-invasive approach. Due to scarcity of physician/chest specialist/expert doctors, technology-enabled disease screening techniques have been developed by several researchers with the help of artificial intelligence and machine learning (AI/ML). It can be remarkably observed that the researchers have introduced several AI/ML/DL (deep learning) algorithms for computer-assisted detection of COVID-19 using chest X-ray and CT images. In this paper, a comprehensive review has been conducted to summarize the works related to applications of AI/ML/DL for diagnostic prediction of COVID-19, mainly using X-ray and CT images. Following the PRISMA guidelines, total 265 articles have been selected out of 1715 published articles till the third quarter of 2021. Furthermore, this review summarizes and compares varieties of ML/DL techniques, various datasets, and their results using X-ray and CT imaging. A detailed discussion has been made on the novelty of the published works, along with advantages and limitations.

COVIDX-LwNet: A Lightweight Network Ensemble Model for the Detection of COVID-19 Based on Chest X-ray Images

Recently, the COVID-19 pandemic coronavirus has put a lot of pressure on health systems around the world. One of the most common ways to detect COVID-19 is to use chest X-ray images, which have the advantage of being cheap and fast. However, in the early days of the COVID-19 outbreak, most studies applied pretrained convolutional neural network (CNN) models, and the features produced by the last convolutional layer were directly passed into the classification head. In this study, the proposed ensemble model consists of three lightweight networks, Xception, MobileNetV2 and NasNetMobile as three original feature extractors, and then three base classifiers are obtained by adding the coordinated attention module, LSTM and a new classification head to the original feature extractors. The classification results from the three base classifiers are then fused by a confidence fusion method. Three publicly available chest X-ray datasets for COVID-19 testing were considered, with ternary (COVID-19, normal and other pneumonia) and quaternary (COVID-19, normal) analyses performed on the first two datasets, bacterial pneumonia and viral pneumonia classification, and achieved high accuracy rates of 95.56% and 91.20%, respectively. The third dataset was used to compare the performance of the model compared to other models and the generalization ability on different datasets. We performed a thorough ablation study on the first dataset to understand the impact of each proposed component. Finally, we also performed visualizations. These saliency maps not only explain key prediction decisions of the model, but also help radiologists locate areas of infection. Through extensive experiments, it was finally found that the results obtained by the proposed method are comparable to the state-of-the-art methods.

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.

A comprehensive review of COVID-19 detection techniques: From laboratory systems to wearable devices

Screening of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among symptomatic and asymptomatic patients offers unique opportunities for curtailing the transmission of novel coronavirus disease 2019, commonly known as COVID-19. Molecular diagnostic techniques, namely reverse transcription loop-mediated isothermal amplification (RT-LAMP), reverse transcription-polymerase chain reaction (RT-PCR), and immunoassays, have been frequently used to identify COVID-19 infection. Although these techniques are robust and accurate, mass testing of potentially infected individuals has shown difficulty due to the resources, manpower, and costs it entails. Moreover, as these techniques are typically used to test symptomatic patients, healthcare systems have failed to screen asymptomatic patients, whereas the spread of COVID-19 by these asymptomatic individuals has turned into a crucial problem. Besides, respiratory infections or cardiovascular conditions generally demonstrate changes in physiological parameters, namely body temperature, blood pressure, and breathing rate, which signifies the onset of diseases. Such vitals monitoring systems have shown promising results employing artificial intelligence (AI). Therefore, the potential use of wearable devices for monitoring asymptomatic COVID-19 individuals has recently been explored. This work summarizes the efforts that have been made in the domains from laboratory-based testing to asymptomatic patient monitoring via wearable systems.

COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

PCovNet: A presymptomatic COVID-19 detection framework using deep learning model using wearables data

While the advanced diagnostic tools and healthcare management protocols have been struggling to contain the COVID-19 pandemic, the spread of the contagious viral pathogen before the symptom onset acted as the Achilles' heel. Although reverse transcription-polymerase chain reaction (RT-PCR) has been widely used for COVID-19 diagnosis, they are hardly administered before any visible symptom, which provokes rapid transmission. This study proposes PCovNet, a Long Short-term Memory Variational Autoencoder (LSTM-VAE)-based anomaly detection framework, to detect COVID-19 infection in the presymptomatic stage from the Resting Heart Rate (RHR) derived from the wearable devices, i.e., smartwatch or fitness tracker. The framework was trained and evaluated in two configurations on a publicly available wearable device dataset consisting of 25 COVID-positive individuals in the span of four months including their COVID-19 infection phase. The first configuration of the framework detected RHR abnormality with average Precision, Recall, and F-beta scores of 0.946, 0.234, and 0.918, respectively. However, the second configuration detected aberrant RHR in 100% of the subjects (25 out of 25) during the infectious period. Moreover, 80% of the subjects (20 out of 25) were detected during the presymptomatic stage. These findings prove the feasibility of using wearable devices with such a deep learning framework as a secondary diagnosis tool to circumvent the presymptomatic COVID-19 detection problem.

CVD-HNet: Classifying Pneumonia and COVID-19 in Chest X-ray Images Using Deep Network

The use of computer-assisted analysis to improve image interpretation has been a long-standing challenge in the medical imaging industry. In terms of image comprehension, Continuous advances in AI (Artificial Intelligence), predominantly in DL (Deep Learning) techniques, are supporting in the classification, Detection, and quantification of anomalies in medical images. DL techniques are the most rapidly evolving branch of AI, and it's recently been successfully pragmatic in a variety of fields, including medicine. This paper provides a classification method for COVID 19 infected X-ray images based on new novel deep CNN model. For COVID19 specified pneumonia analysis, two new customized CNN architectures, CVD-HNet1 (COVID-HybridNetwork1) and CVD-HNet2 (COVID-HybridNetwork2), have been designed. The suggested method utilizes operations based on boundaries and regions, as well as convolution processes, in a systematic manner. In comparison to existing CNNs, the suggested classification method achieves excellent Accuracy 98 percent, F Score 0.99 and MCC 0.97. These results indicate impressive classification accuracy on a limited dataset, with more training examples, much better results can be achieved. Overall, our CVD-HNet model could be a useful tool for radiologists in diagnosing and detecting COVID 19 instances early.

An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction

Deep learning models demonstrate superior performance in image classification problems. COVID-19 image classification is developed using single deep learning models. In this paper, an efficient hardware architecture based on an ensemble deep learning model is built to identify the COVID-19 using chest X-ray (CXR) records. Five deep learning models namely ResNet, fitness, IRCNN (Inception Recurrent Convolutional Neural Network), effectiveness, and Fitnet are ensembled for fine-tuning and enhancing the performance of the COVID-19 identification; these models are chosen as they individually perform better in other applications. Experimental analysis shows that the accuracy, precision, recall, and F1 for COVID-19 detection are 0.99,0.98,0.98, and 0.98 respectively. An application-specific hardware architecture incorporates the pipeline, parallel processing, reusability of computational resources by carefully exploiting the data flow and resource availability. The processing element (PE) and the CNN architecture are modeled using Verilog, simulated, and synthesized using cadence with Taiwan Semiconductor Manufacturing Co Ltd (TSMC) 90 nm tech file. The simulated results show a 40% reduction in the latency and number of clock cycles. The computations and power consumptions are minimized by designing the PE as a data-aware unit. Thus, the proposed architecture is best suited for Covid-19 prediction and diagnosis.

Let AI Perform Better Next Time—A Systematic Review of Medical Imaging-Based Automated Diagnosis of COVID-19: 2020–2022

The pandemic of COVID-19 has caused millions of infections, which has led to a great loss all over the world, socially and economically. Due to the false-negative rate and the time-consuming characteristic of the Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests, diagnosing based on X-ray images and Computed Tomography (CT) images has been widely adopted to confirm positive COVID-19 RT-PCR tests. Since the very beginning of the pandemic, researchers in the artificial intelligence area have proposed a large number of automatic diagnosing models, hoping to assist radiologists and improve the diagnosing accuracy. However, after two years of development, there are still few models that can actually be applied in real-world scenarios. Numerous problems have emerged in the research of the automated diagnosis of COVID-19. In this paper, we present a systematic review of these diagnosing models. A total of 179 proposed models are involved. First, we compare the medical image modalities (CT or X-ray) for COVID-19 diagnosis from both the clinical perspective and the artificial intelligence perspective. Then, we classify existing methods into two types—image-level diagnosis (i.e., classification-based methods) and pixel-level diagnosis (i.e., segmentation-based models). For both types of methods, we define universal model pipelines and analyze the techniques that have been applied in each step of the pipeline in detail. In addition, we also review some commonly adopted public COVID-19 datasets. More importantly, we present an in-depth discussion of the existing automated diagnosis models and note a total of three significant problems: biased model performance evaluation; inappropriate implementation details; and a low reproducibility, reliability and explainability. For each point, we give corresponding recommendations on how we can avoid making the same mistakes and let AI perform better in the next pandemic.

BEMD-3DCNN-based method for COVID-19 detection

The coronavirus outbreak continues to spread around the world and no one knows when it will stop. Therefore, from the first day of the identification of the virus in Wuhan, China, scientists have launched numerous research projects to understand the nature of the virus, how to detect it, and search for the most effective medicine to help and protect patients. Importantly, a rapid diagnostic and detection system is a priority and should be developed to stop COVID-19 from spreading. Medical imaging techniques have been used for this purpose. Current research is focused on exploiting different backbones like VGG, ResNet, DenseNet, or combining them to detect COVID-19. By using these backbones many aspects cannot be analyzed like the spatial and contextual information in the images, although this information can be useful for more robust detection performance. In this paper, we used 3D representation of the data as input for the proposed 3DCNN-based deep learning model. The process includes using the Bi-dimensional Empirical Mode Decomposition (BEMD) technique to decompose the original image into IMFs, and then building a video of these IMF images. The formed video is used as input for the 3DCNN model to classify and detect the COVID-19 virus. The 3DCNN model consists of a 3D VGG-16 backbone followed by a Context-aware attention (CAA) module, and then fully connected layers for classification. Each CAA module takes the feature maps of different blocks of the backbone, which allows learning from different feature maps. In our experiments, we used 6484 X-ray images, of which 1802 were COVID-19 positive cases, 1910 normal cases, and 2772 pneumonia cases. The experiment results showed that our proposed technique achieved the desired results on the selected dataset. Additionally, the use of the 3DCNN model with contextual information processing exploited CAA networks to achieve better performance.

Multi-Classification of Chest X-rays for COVID-19 Diagnosis Using Deep Learning Algorithms

Accurate detection of COVID-19 is of immense importance to help physicians intervene with appropriate treatments. Although RT-PCR is routinely used for COVID-19 detection, it is expensive, takes a long time, and is prone to inaccurate results. Currently, medical imaging-based detection systems have been explored as an alternative for more accurate diagnosis. In this work, we propose a multi-level diagnostic framework for the accurate detection of COVID-19 using X-ray scans based on transfer learning. The developed framework consists of three stages, beginning with a pre-processing step to remove noise effects and image resizing followed by a deep learning architecture utilizing an Xception pre-trained model for feature extraction from the pre-processed image. Our design utilizes a global average pooling (GAP) layer for avoiding over-fitting, and an activation layer is added in order to reduce the losses. Final classification is achieved using a softmax layer. The system is evaluated using different activation functions and thresholds with different optimizers. We used a benchmark dataset from the kaggle website. The proposed model has been evaluated on 7395 images that consist of 3 classes (COVID-19, normal and pneumonia). Additionally, we compared our framework with the traditional pre-trained deep learning models and with other literature studies. Our evaluation using various metrics showed that our framework achieved a high test accuracy of 99.3% with a minimum loss of 0.02 using the LeakyReLU activation function at a threshold equal to 0.1 with the RMSprop optimizer. Additionally, we achieved a sensitivity and specificity of 99 and F1-Score of 99.3% with only 10 epochs and a 10−4 learning rate.

Multi-Channel Based Image Processing Scheme for Pneumonia Identification

Pneumonia is a prevalent severe respiratory infection that affects the distal and alveoli airways. Across the globe, it is a serious public health issue that has caused high mortality rate of children below five years old and the aged citizens who must have had previous chronic-related ailment. Pneumonia can be caused by a wide range of microorganisms, including virus, fungus, bacteria, which varies greatly across the globe. The spread of the ailment has gained computer-aided diagnosis (CAD) attention. This paper presents a multi-channel-based image processing scheme to automatically extract features and identify pneumonia from chest X-ray images. The proposed approach intends to address the problem of low quality and identify pneumonia in CXR images. Three channels of CXR images, namely, the Local Binary Pattern (LBP), Contrast Enhanced Canny Edge Detection (CECED), and Contrast Limited Adaptive Histogram Equalization (CLAHE) CXR images are processed by deep neural networks. CXR-related features of LBP images are extracted using shallow CNN, features of the CLAHE CXR images are extracted by pre-trained inception-V3, whereas the features of CECED CXR images are extracted using pre-trained MobileNet-V3. The final feature weights of the three channels are concatenated and softmax classification is utilized to determine the final identification result. The proposed network can accurately classify pneumonia according to the experimental result. The proposed method tested on publicly available dataset reports accuracy of 98.3%, sensitivity of 98.9%, and specificity of 99.2%. Compared with the single models and the state-of-the-art models, our proposed network achieves comparable performance.

Deep Learning for Reliable Classification of COVID-19, MERS, and SARS from Chest X-ray Images

Novel coronavirus disease (COVID-19) is an extremely contagious and quickly spreading coronavirus infestation. Severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS), which outbreak in 2002 and 2011, and the current COVID-19 pandemic are all from the same family of coronavirus. This work aims to classify COVID-19, SARS, and MERS chest X-ray (CXR) images using deep convolutional neural networks (CNNs). To the best of our knowledge, this classification scheme has never been investigated in the literature. A unique database was created, so-called QU-COVID-family, consisting of 423 COVID-19, 144 MERS, and 134 SARS CXR images. Besides, a robust COVID-19 recognition system was proposed to identify lung regions using a CNN segmentation model (U-Net), and then classify the segmented lung images as COVID-19, MERS, or SARS using a pre-trained CNN classifier. Furthermore, the Score-CAM visualization method was utilized to visualize classification output and understand the reasoning behind the decision of deep CNNs. Several deep learning classifiers were trained and tested; four outperforming algorithms were reported: SqueezeNet, ResNet18, InceptionV3, and DenseNet201. Original and preprocessed images were used individually and all together as the input(s) to the networks. Two recognition schemes were considered: plain CXR classification and segmented CXR classification. For plain CXRs, it was observed that InceptionV3 outperforms other networks with a 3-channel scheme and achieves sensitivities of 99.5%, 93.1%, and 97% for classifying COVID-19, MERS, and SARS images, respectively. In contrast, for segmented CXRs, InceptionV3 outperformed using the original CXR dataset and achieved sensitivities of 96.94%, 79.68%, and 90.26% for classifying COVID-19, MERS, and SARS images, respectively. The classification performance degrades with segmented CXRs compared to plain CXRs. However, the results are more reliable as the network learns from the main region of interest, avoiding irrelevant non-lung areas (heart, bones, or text), which was confirmed by the Score-CAM visualization. All networks showed high COVID-19 detection sensitivity (> 96%) with the segmented lung images. This indicates the unique radiographic signature of COVID-19 cases in the eyes of AI, which is often a challenging task for medical doctors.

COVID-19 infection localization and severity grading from chest X-ray images

The immense spread of coronavirus disease 2019 (COVID-19) has left healthcare systems incapable to diagnose and test patients at the required rate. Given the effects of COVID-19 on pulmonary tissues, chest radiographic imaging has become a necessity for screening and monitoring the disease. Numerous studies have proposed Deep Learning approaches for the automatic diagnosis of COVID-19. Although these methods achieved outstanding performance in detection, they have used limited chest X-ray (CXR) repositories for evaluation, usually with a few hundred COVID-19 CXR images only. Thus, such data scarcity prevents reliable evaluation of Deep Learning models with the potential of overfitting. In addition, most studies showed no or limited capability in infection localization and severity grading of COVID-19 pneumonia. In this study, we address this urgent need by proposing a systematic and unified approach for lung segmentation and COVID-19 localization with infection quantification from CXR images. To accomplish this, we have constructed the largest benchmark dataset with 33,920 CXR images, including 11,956 COVID-19 samples, where the annotation of ground-truth lung segmentation masks is performed on CXRs by an elegant human-machine collaborative approach. An extensive set of experiments was performed using the state-of-the-art segmentation networks, U-Net, U-Net++, and Feature Pyramid Networks (FPN). The developed network, after an iterative process, reached a superior performance for lung region segmentation with Intersection over Union (IoU) of 96.11% and Dice Similarity Coefficient (DSC) of 97.99%. Furthermore, COVID-19 infections of various shapes and types were reliably localized with 83.05% IoU and 88.21% DSC. Finally, the proposed approach has achieved an outstanding COVID-19 detection performance with both sensitivity and specificity values above 99%.

COVID-19 Detection Using Deep Learning Algorithm on Chest X-ray Images

COVID-19, regarded as the deadliest virus of the 21st century, has claimed the lives of millions of people around the globe in less than two years. Since the virus initially affects the lungs of patients, X-ray imaging of the chest is helpful for effective diagnosis. Any method for automatic, reliable, and accurate screening of COVID-19 infection would be beneficial for rapid detection and reducing medical or healthcare professional exposure to the virus. In the past, Convolutional Neural Networks (CNNs) proved to be quite successful in the classification of medical images. In this study, an automatic deep learning classification method for detecting COVID-19 from chest X-ray images is suggested using a CNN. A dataset consisting of 3616 COVID-19 chest X-ray images and 10,192 healthy chest X-ray images was used. The original data were then augmented to increase the data sample to 26,000 COVID-19 and 26,000 healthy X-ray images. The dataset was enhanced using histogram equalization, spectrum, grays, cyan and normalized with NCLAHE before being applied to CNN models. Initially using the dataset, the symptoms of COVID-19 were detected by employing eleven existing CNN models; VGG16, VGG19, MobileNetV2, InceptionV3, NFNet, ResNet50, ResNet101, DenseNet, EfficientNetB7, AlexNet, and GoogLeNet. From the models, MobileNetV2 was selected for further modification to obtain a higher accuracy of COVID-19 detection. Performance evaluation of the models was demonstrated using a confusion matrix. It was observed that the modified MobileNetV2 model proposed in the study gave the highest accuracy of 98% in classifying COVID-19 and healthy chest X-rays among all the implemented CNN models. The second-best performance was achieved from the pre-trained MobileNetV2 with an accuracy of 97%, followed by VGG19 and ResNet101 with 95% accuracy for both the models. The study compares the compilation time of the models. The proposed model required the least compilation time with 2 h, 50 min and 21 s. Finally, the Wilcoxon signed-rank test was performed to test the statistical significance. The results suggest that the proposed method can efficiently identify the symptoms of infection from chest X-ray images better than existing methods.

COVID-19 Pneumonia Detection Using Optimized Deep Learning Techniques

It became apparent that mankind has to learn to live with and adapt to COVID-19, especially because the developed vaccines thus far do not prevent the infection but rather just reduce the severity of the symptoms. The manual classification and diagnosis of COVID-19 pneumonia requires specialized personnel and is time consuming and very costly. On the other hand, automatic diagnosis would allow for real-time diagnosis without human intervention resulting in reduced costs. Therefore, the objective of this research is to propose a novel optimized Deep Learning (DL) approach for the automatic classification and diagnosis of COVID-19 pneumonia using X-ray images. For this purpose, a publicly available dataset of chest X-rays on Kaggle was used in this study. The dataset was developed over three stages in a quest to have a unified COVID-19 entities dataset available for researchers. The dataset consists of 21,165 anterior-to-posterior and posterior-to-anterior chest X-ray images classified as: Normal (48%), COVID-19 (17%), Lung Opacity (28%) and Viral Pneumonia (6%). Data Augmentation was also applied to increase the dataset size to enhance the reliability of results by preventing overfitting. An optimized DL approach is implemented in which chest X-ray images go through a three-stage process. Image Enhancement is performed in the first stage, followed by Data Augmentation stage and in the final stage the results are fed to the Transfer Learning algorithms (AlexNet, GoogleNet, VGG16, VGG19, and DenseNet) where the images are classified and diagnosed. Extensive experiments were performed under various scenarios, which led to achieving the highest classification accuracy of 95.63% through the application of VGG16 transfer learning algorithm on the augmented enhanced dataset with freeze weights. This accuracy was found to be better as compared to the results reported by other methods in the recent literature. Thus, the proposed approach proved superior in performance as compared with that of other similar approaches in the extant literature, and it made a valuable contribution to the body of knowledge. Although the results achieved so far are promising, further work is planned to correlate the results of the proposed approach with clinical observations to further enhance the efficiency and accuracy of COVID-19 diagnosis.

A Comprehensive Survey of COVID-19 Detection Using Medical Images

The outbreak of the Coronavirus disease 2019 (COVID-19) caused the death of a large number of people and declared as a pandemic by the World Health Organization. Millions of people are infected by this virus and are still getting infected every day. As the cost and required time of conventional Reverse Transcription Polymerase Chain Reaction (RT-PCR) tests to detect COVID-19 is uneconomical and excessive, researchers are trying to use medical images such as X-ray and Computed Tomography (CT) images to detect this disease with the help of Artificial Intelligence (AI)-based systems, to assist in automating the scanning procedure. In this paper, we reviewed some of these newly emerging AI-based models that can detect COVID-19 from X-ray or CT of lung images. We collected information about available research resources and inspected a total of 80 papers till June 20, 2020. We explored and analyzed data sets, preprocessing techniques, segmentation methods, feature extraction, classification, and experimental results which can be helpful for finding future research directions in the domain of automatic diagnosis of COVID-19 disease using AI-based frameworks. It is also reflected that there is a scarcity of annotated medical images/data sets of COVID-19 affected people, which requires enhancing, segmentation in preprocessing, and domain adaptation in transfer learning for a model, producing an optimal result in model performance. This survey can be the starting point for a novice/beginner level researcher to work on COVID-19 classification.

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

Since the first case of coronavirus disease 2019 (COVID-19) was discovered in December 2019, COVID-19 swiftly spread over the world. By the end of March 2021, more than 136 million patients have been infected. Since the second and third waves of the COVID-19 outbreak are in full swing, investigating effective and timely solutions for patients' check-ups and treatment is important. Although the SARS-CoV-2 virus-specific reverse transcription polymerase chain reaction test is recommended for the diagnosis of COVID-19, the test results are prone to be false negative in the early course of COVID-19 infection. To enhance the screening efficiency and accessibility, chest images captured via X-ray or computed tomography (CT) provide valuable information when evaluating patients with suspected COVID-19 infection. With advanced artificial intelligence (AI) techniques, AI-driven models training with lung scans emerge as quick diagnostic and screening tools for detecting COVID-19 infection in patients. In this article, we provide a comprehensive review of state-of-the-art AI-empowered methods for computational examination of COVID-19 patients with lung scans. In this regard, we searched for papers and preprints on bioRxiv, medRxiv, and arXiv published for the period from January 1, 2020, to March 31, 2021, using the keywords of COVID, lung scans, and AI. After the quality screening, 96 studies are included in this review. The reviewed studies were grouped into three categories based on their target application scenarios: automatic detection of coronavirus disease, infection segmentation, and severity assessment and prognosis prediction. The latest AI solutions to process and analyze chest images for COVID-19 treatment and their advantages and limitations are presented. In addition to reviewing the rapidly developing techniques, we also summarize publicly accessible lung scan image sets. The article ends with discussions of the challenges in current research and potential directions in designing effective computational solutions to fight against the COVID-19 pandemic in the future.

Deep-chest: Multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer chest diseases

Corona Virus Disease (COVID-19) has been announced as a pandemic and is spreading rapidly throughout the world. Early detection of COVID-19 may protect many infected people. Unfortunately, COVID-19 can be mistakenly diagnosed as pneumonia or lung cancer, which with fast spread in the chest cells, can lead to patient death. The most commonly used diagnosis methods for these three diseases are chest X-ray and computed tomography (CT) images. In this paper, a multi-classification deep learning model for diagnosing COVID-19, pneumonia, and lung cancer from a combination of chest x-ray and CT images is proposed. This combination has been used because chest X-ray is less powerful in the early stages of the disease, while a CT scan of the chest is useful even before symptoms appear, and CT can precisely detect the abnormal features that are identified in images. In addition, using these two types of images will increase the dataset size, which will increase the classification accuracy. To the best of our knowledge, no other deep learning model choosing between these diseases is found in the literature. In the present work, the performance of four architectures are considered, namely: VGG19-CNN, ResNet152V2, ResNet152V2 + Gated Recurrent Unit (GRU), and ResNet152V2 + Bidirectional GRU (Bi-GRU). A comprehensive evaluation of different deep learning architectures is provided using public digital chest x-ray and CT datasets with four classes (i.e., Normal, COVID-19, Pneumonia, and Lung cancer). From the results of the experiments, it was found that the VGG19 +CNN model outperforms the three other proposed models. The VGG19+CNN model achieved 98.05% accuracy (ACC), 98.05% recall, 98.43% precision, 99.5% specificity (SPC), 99.3% negative predictive value (NPV), 98.24% F1 score, 97.7% Matthew's correlation coefficient (MCC), and 99.66% area under the curve (AUC) based on X-ray and CT images.

Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images

Computer-aided diagnosis for the reliable and fast detection of coronavirus disease (COVID-19) has become a necessity to prevent the spread of the virus during the pandemic to ease the burden on the healthcare system. Chest X-ray (CXR) imaging has several advantages over other imaging and detection techniques. Numerous works have been reported on COVID-19 detection from a smaller set of original X-ray images. However, the effect of image enhancement and lung segmentation of a large dataset in COVID-19 detection was not reported in the literature. We have compiled a large X-ray dataset (COVQU) consisting of 18,479 CXR images with 8851 normal, 6012 non-COVID lung infections, and 3616 COVID-19 CXR images and their corresponding ground truth lung masks. To the best of our knowledge, this is the largest public COVID positive database and the lung masks. Five different image enhancement techniques: histogram equalization (HE), contrast limited adaptive histogram equalization (CLAHE), image complement, gamma correction, and balance contrast enhancement technique (BCET) were used to investigate the effect of image enhancement techniques on COVID-19 detection. A novel U-Net model was proposed and compared with the standard U-Net model for lung segmentation. Six different pre-trained Convolutional Neural Networks (CNNs) (ResNet18, ResNet50, ResNet101, InceptionV3, DenseNet201, and ChexNet) and a shallow CNN model were investigated on the plain and segmented lung CXR images. The novel U-Net model showed an accuracy, Intersection over Union (IoU), and Dice coefficient of 98.63%, 94.3%, and 96.94%, respectively for lung segmentation. The gamma correction-based enhancement technique outperforms other techniques in detecting COVID-19 from the plain and the segmented lung CXR images. Classification performance from plain CXR images is slightly better than the segmented lung CXR images; however, the reliability of network performance is significantly improved for the segmented lung images, which was observed using the visualization technique. The accuracy, precision, sensitivity, F1-score, and specificity were 95.11%, 94.55%, 94.56%, 94.53%, and 95.59% respectively for the segmented lung images. The proposed approach with very reliable and comparable performance will boost the fast and robust COVID-19 detection using chest X-ray images.

COVID-19 infection map generation and detection from chest X-ray images

Computer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

Advance Warning Methodologies for COVID-19 Using Chest X-Ray Images

Coronavirus disease 2019 (COVID-19) has rapidly become a global health concern after its first known detection in December 2019. As a result, accurate and reliable advance warning system for the early diagnosis of COVID-19 has now become a priority. The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage. In this study, our first aim is to evaluate the ability of recent state-of-the-art Machine Learning techniques for the early detection of COVID-19 from chest X-ray images. Both compact classifiers and deep learning approaches are considered in this study. Furthermore, we propose a recent compact classifier, Convolutional Support Estimator Network (CSEN) approach for this purpose since it is well-suited for a scarce-data classification task. Finally, this study introduces a new benchmark dataset called Early-QaTa-COV19, which consists of 1065 early-stage COVID-19 pneumonia samples (very limited or no infection signs) labeled by the medical doctors and 12544 samples for control (normal) class. A detailed set of experiments shows that the CSEN achieves the top (over 97%) sensitivity with over 95.5% specificity. Moreover, DenseNet-121 network produces the leading performance among other deep networks with 95% sensitivity and 99.74% specificity.

Deep-LSTM ensemble framework to forecast Covid-19: an insight to the global pandemic

The pandemic of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is spreading all over the world. Medical health care systems are in urgent need to diagnose this pandemic with the support of new emerging technologies like artificial intelligence (AI), internet of things (IoT) and Big Data System. In this dichotomy study, we divide our research in two ways-firstly, the review of literature is carried out on databases of Elsevier, Google Scholar, Scopus, PubMed and Wiley Online using keywords Coronavirus, Covid-19, artificial intelligence on Covid-19, Coronavirus 2019 and collected the latest information about Covid-19. Possible applications are identified from the same to enhance the future research. We have found various databases, websites and dashboards working on real time extraction of Covid-19 data. This will be conducive for future research to easily locate the available information. Secondly, we designed a nested ensemble model using deep learning methods based on long short term memory (LSTM). Proposed Deep-LSTM ensemble model is evaluated on intensive care Covid-19 confirmed and death cases of India with different classification metrics such as accuracy, precision, recall, f-measure and mean absolute percentage error. Medical healthcare facilities are boosted with the intervention of AI as it can mimic human intelligence. Contactless treatment is possible only with the help of AI assisted automated health care systems. Furthermore, remote location self treatment is one of the key benefits provided by AI based systems.

Applications of artificial intelligence in battling against covid-19: A literature review

Colloquially known as coronavirus, the Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), that causes CoronaVirus Disease 2019 (COVID-19), has become a matter of grave concern for every country around the world. The rapid growth of the pandemic has wreaked havoc and prompted the need for immediate reactions to curb the effects. To manage the problems, many research in a variety of area of science have started studying the issue. Artificial Intelligence is among the area of science that has found great applications in tackling the problem in many aspects. Here, we perform an overview on the applications of AI in a variety of fields including diagnosis of the disease via different types of tests and symptoms, monitoring patients, identifying severity of a patient, processing covid-19 related imaging tests, epidemiology, pharmaceutical studies, etc. The aim of this paper is to perform a comprehensive survey on the applications of AI in battling against the difficulties the outbreak has caused. Thus we cover every way that AI approaches have been employed and to cover all the research until the writing of this paper. We try organize the works in a way that overall picture is comprehensible. Such a picture, although full of details, is very helpful in understand where AI sits in current pandemonium. We also tried to conclude the paper with ideas on how the problems can be tackled in a better way and provide some suggestions for future works.