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

Mortality Prediction Utilizing Blood Biomarkers to Predict the Severity of COVID-19 Using Machine Learning Technique

Healthcare researchers have been working on mortality prediction for COVID-19 patients with differing levels of severity. A rapid and reliable clinical evaluation of disease intensity will assist in the allocation and prioritization of mortality mitigation resources. The novelty of the work proposed in this paper is an early prediction model of high mortality risk for both COVID-19 and non-COVID-19 patients, which provides state-of-the-art performance, in an external validation cohort from a different population. Retrospective research was performed on two separate hospital datasets from two different countries for model development and validation. In the first dataset, COVID-19 and non-COVID-19 patients were admitted to the emergency department in Boston (24 March 2020 to 30 April 2020), and in the second dataset, 375 COVID-19 patients were admitted to Tongji Hospital in China (10 January 2020 to 18 February 2020). The key parameters to predict the risk of mortality for COVID-19 and non-COVID-19 patients were identified and a nomogram-based scoring technique was developed using the top-ranked five parameters. Age, Lymphocyte count, D-dimer, CRP, and Creatinine (ALDCC), information acquired at hospital admission, were identified by the logistic regression model as the primary predictors of hospital death. For the development cohort, and internal and external validation cohorts, the area under the curves (AUCs) were 0.987, 0.999, and 0.992, respectively. All the patients are categorized into three groups using ALDCC score and death probability: Low (probability < 5%), Moderate (5% < probability < 50%), and High (probability > 50%) risk groups. The prognostic model, nomogram, and ALDCC score will be able to assist in the early identification of both COVID-19 and non-COVID-19 patients with high mortality risk, helping physicians to improve patient management.

Automatic detection of COVID-19 using pruned GLCM-Based texture features and LDCRF classification

Recently, automatic computer-aided detection (CAD) of COVID-19 using radiological images has received a great deal of attention from many researchers and medical practitioners, and consequently several CAD frameworks and methods have been presented in the literature to assist the radiologist physicians in performing diagnostic COVID-19 tests quickly, reliably and accurately. This paper presents an innovative framework for the automatic detection of COVID-19 from chest X-ray (CXR) images, in which a rich and effective representation of lung tissue patterns is generated from the gray level co-occurrence matrix (GLCM) based textural features. The input CXR image is first preprocessed by spatial filtering along with median filtering and contrast limited adaptive histogram equalization to improve the CXR image's poor quality and reduce image noise. Automatic thresholding by the optimized formula of Otsu's method is applied to find a proper threshold value to best segment lung regions of interest (ROIs) out from CXR images. Then, a concise set of GLCM-based texture features is extracted to accurately represent the segmented lung ROIs of each CXR image. Finally, the normalized features are fed into a trained discriminative latent-dynamic conditional random fields (LDCRFs) model for fine-grained classification to divide the cases into two categories: COVID-19 and non-COVID-19. The presented method has been experimentally tested and validated on a relatively large dataset of frontal CXR images, achieving an average accuracy, precision, recall, and F1-score of 95.88%, 96.17%, 94.45%, and 95.79%, respectively, which compare favorably with and occasionally exceed those previously reported in similar studies in the literature.

Detection of COVID-19 Using Transfer Learning and Grad-CAM Visualization on Indigenously Collected X-ray Dataset

The COVID-19 outbreak began in December 2019 and has dreadfully affected our lives since then. More than three million lives have been engulfed by this newest member of the corona virus family. With the emergence of continuously mutating variants of this virus, it is still indispensable to successfully diagnose the virus at early stages. Although the primary technique for the diagnosis is the PCR test, the non-contact methods utilizing the chest radiographs and CT scans are always preferred. Artificial intelligence, in this regard, plays an essential role in the early and accurate detection of COVID-19 using pulmonary images. In this research, a transfer learning technique with fine tuning was utilized for the detection and classification of COVID-19. Four pre-trained models i.e., VGG16, DenseNet-121, ResNet-50, and MobileNet were used. The aforementioned deep neural networks were trained using the dataset (available on Kaggle) of 7232 (COVID-19 and normal) chest X-ray images. An indigenous dataset of 450 chest X-ray images of Pakistani patients was collected and used for testing and prediction purposes. Various important parameters, e.g., recall, specificity, F1-score, precision, loss graphs, and confusion matrices were calculated to validate the accuracy of the models. The achieved accuracies of VGG16, ResNet-50, DenseNet-121, and MobileNet are 83.27%, 92.48%, 96.49%, and 96.48%, respectively. In order to display feature maps that depict the decomposition process of an input image into various filters, a visualization of the intermediate activations is performed. Finally, the Grad-CAM technique was applied to create class-specific heatmap images in order to highlight the features extracted in the X-ray images. Various optimizers were used for error minimization purposes. DenseNet-121 outperformed the other three models in terms of both accuracy and prediction.

Accurate detection of COVID-19 using deep features based on X-Ray images and feature selection methods

COVID-19 is a severe epidemic affecting the whole world. This epidemic, which has a high mortality rate, affects the health systems and the economies of countries significantly. Therefore, ending the epidemic is one of the most important priorities of all states. For this, automatic diagnosis and detection systems are very important to control the epidemic. In addition to the recommendation of the “reverse transcription-polymerase chain reaction (RT-PCR)” test, additional diagnosis and detection systems are required. Hence, based on the fact that the COVID-19 virus attacks the lungs, automatic diagnosis and detection systems developed using X-ray and CT images come to the fore. In this study, a high-performance detection system was implemented with three different CNN (ResNet50, ResNet101, InceptionResNetV2) models and X-ray images of three different classes (COVID-19, Normal, Pneumonia). The particle swarm optimization (PSO) algorithm and ant colony algorithm (ACO) was applied among the feature selection methods, and their performances were compared. The results were obtained using support vector machines (SVM) and a k-nearest neighbor (k-NN) classifier using the 10-fold cross-validation method. The highest overall accuracy performance was 99.83% with the SVM algorithm without feature selection. The highest performance was achieved after the feature selection process with the SVM + PSO method as 99.86%. As a result, higher performance with less computational load has been achieved by realizing the feature selection. Based on the high results obtained, it is thought that this study will benefit radiologists as a decision support system.

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.

On the Use of Deep Learning for Imaging-Based COVID-19 Detection Using Chest X-rays

The global COVID-19 pandemic that started in 2019 and created major disruptions around the world demonstrated the imperative need for quick, inexpensive, accessible and reliable diagnostic methods that would allow the detection of infected individuals with minimal resources. Radiography, and more specifically, chest radiography, is a relatively inexpensive medical imaging modality that can potentially offer a solution for the diagnosis of COVID-19 cases. In this work, we examined eleven deep convolutional neural network architectures for the task of classifying chest X-ray images as belonging to healthy individuals, individuals with COVID-19 or individuals with viral pneumonia. All the examined networks are established architectures that have been proven to be efficient in image classification tasks, and we evaluated three different adjustments to modify the architectures for the task at hand by expanding them with additional layers. The proposed approaches were evaluated for all the examined architectures on a dataset with real chest X-ray images, reaching the highest classification accuracy of 98.04% and the highest F1-score of 98.22% for the best-performing setting.

Application of deep learning to identify COVID-19 infection in posteroanterior chest X-rays

Introduction

The objective of this study was to assess seven configurations of six convolutional deep neural network architectures for classification of chest X-rays (CXRs) as COVID-19 positive or negative.

Methods

The primary dataset consisted of 294 COVID-19 positive and 294 COVID-19 negative CXRs, the latter comprising roughly equally many pneumonia, emphysema, fibrosis, and healthy images. We used six common convolutional neural network architectures, VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile and InceptionV3. We studied six models (one for each architecture) which were pre-trained on a vast repository of generic (non-CXR) images, as well as a seventh DenseNet121 model, which was pre-trained on a repository of CXR images. For each model, we replaced the output layers with custom fully connected layers for the task of binary classification of images as COVID-19 positive or negative. Performance metrics were calculated on a hold-out test set with CXRs from patients who were not included in the training/validation set.

Results

When pre-trained on generic images, the VGG16, DenseNet121, DenseNet201, MobileNet, NasNetMobile, and InceptionV3 architectures respectively produced hold-out test set areas under the receiver operating characteristic (AUROCs) of 0.98, 0.95, 0.97, 0.95, 0.99, and 0.96 for the COVID-19 classification of CXRs. The X-ray pre-trained DenseNet121 model, in comparison, had a test set AUROC of 0.87.

Discussion

Common convolutional neural network architectures with parameters pre-trained on generic images yield high-performance and well-calibrated COVID-19 CXR classification.

Transfer Learning for the Detection and Diagnosis of Types of Pneumonia including Pneumonia Induced by COVID-19 from Chest X-ray Images

Accurate early diagnosis of COVID-19 viral pneumonia, primarily in asymptomatic people, is essential to reduce the spread of the disease, the burden on healthcare capacity, and the overall death rate. It is essential to design affordable and accessible solutions to distinguish pneumonia caused by COVID-19 from other types of pneumonia. In this work, we propose a reliable approach based on deep transfer learning that requires few computations and converges faster. Experimental results demonstrate that our proposed framework for transfer learning is a potential and effective approach to detect and diagnose types of pneumonia from chest X-ray images with a test accuracy of 94.0%.

COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors

Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ray (CXR) is a promising method for early diagnoses of pulmonary diseases. Recently, deep learning has achieved great success in supervised lung segmentation. However, how to effectively utilize the lung region in screening COVID-19 still remains a challenge due to domain shift and lack of manual pixel-level annotations. We hereby propose a multi-appearance COVID-19 screening framework by using lung region priors derived from CXR images. Firstly, we propose a multi-scale adversarial domain adaptation network (MS-AdaNet) to boost the cross-domain lung segmentation task as the prior knowledge to the classification network. Then, we construct a multi-appearance network (MA-Net), which is composed of three sub-networks to realize multi-appearance feature extraction and fusion using lung region priors. At last, we can obtain prediction results from normal, viral pneumonia, and COVID-19 using the proposed MA-Net. We extend the proposed MS-AdaNet for lung segmentation task on three different public CXR datasets. The results suggest that the MS-AdaNet outperforms contrastive methods in cross-domain lung segmentation. Moreover, experiments reveal that the proposed MA-Net achieves accuracy of 98.83% and F1-score of 98.71% on COVID-19 screening. The results indicate that the proposed MA-Net can obtain significant performance on COVID-19 screening.

Deep Transfer Learning Based Unified Framework for COVID19 Classification and Infection Detection from Chest X-Ray Images

The presentation of the COVID19 has endangered several million lives worldwide causing thousands of deaths every day. Evolution of COVID19 as a pandemic calls for automated solutions for initial screening and treatment management. In addition to the thermal scanning mechanisms, findings from chest X-ray imaging examinations are reliable predictors in COVID19 detection, long-term monitoring and severity evaluation. This paper presents a novel deep transfer learning based framework for COVID19 detection and segmentation of infections from chest X-ray images. It is realized as a two-stage cascaded framework with classifier and segmentation subnetwork models. The classifier is modeled as a fine-tuned residual SqueezeNet network, and the segmentation network is implemented as a fine-tuned SegNet semantic segmentation network. The segmentation task is enhanced with a bioinspired Gaussian Mixture Model-based super pixel segmentation. This framework is trained and tested with two public datasets for binary and multiclass classifications and infection segmentation. It achieves accuracies of 99.69% and 99.48% for binary and three class classifications, and a mean accuracy of 83.437% for segmentation. Experimental results and comparative evaluations demonstrate the superiority of this unified model and signify potential extensions for biomarker definition and severity quantization.

Diagnostic accuracy of sagittal TSE-T2W, variable flip angle 3D TSE-T2W and high-resolution 3D heavily T2W sequences for the stenosis of two localizations: the cerebral aqueduct and the superior medullary velum

OBJECTIVES

This study aimed to investigate the accuracy of conventional sagittal turbo spin echo T2-weighted (Sag TSE-T2W), variable flip angle 3D TSE (VFA-3D-TSE) and high-resolution 3D heavily T2W (HR-3D-HT2W) sequences in the diagnosis of primary aqueductal stenosis (PAS) and superior medullary velum stenosis (SMV-S), and the effect of stenosis localization on diagnosis.

METHODS

Seventy-seven patients were included in the study. The diagnosis accuracy of the HR-3D-HT2W, Sag TSE-T2W and VFA-3D-TSE sequences, was classified into three grades by two experienced neuroradiologists: grade 0 (the sequence has no diagnostic ability), grade 1 (the sequence diagnoses stenosis but does not show focal stenosis itself or membrane formation), and grade 2 (the sequence makes a definitive diagnosis of stenosis and shows focal stenosis itself or membrane formation). Stenosis localizations were divided into three as Cerebral Aquaduct (CA), superior medullary velum (SMV) and SMV+CA. In the statistical analysis, the grades of the sequences were compared without making a differentiation based on localization. Then, the effect of localization on diagnosis was determined by comparing the grades for individual localizations.

RESULTS

In the sequence comparison, grade 0 was not detected in the VFA-3D-TSE and HR-3D-HT2W sequences, and these sequences diagnosed all cases. On the other hand, 25.4% of grade 0 was detected with the Sag TSE-T2W sequence (P<0.05). Grade 1 was detected by VFA-3D-TSE in 23% of the cases, while grade 1 (12.5%) was detected by HRH-3D-T2W in only one case, and the difference was statistically significant (P<0.05). When the sequences were examined according to localizations, the rate of grade 0 in the Sag TSE-T2W sequence was statistically significantly higher for the SMV localization (33.3%) compared to CA (66.7%) and SMV+CA (0%) (P<0.05). Localization had no effect on diagnosis using the other sequences.

CONCLUSION

In our study, we found that the VFA-3D-TSE and HR-3D-HT2W sequences were successful in the diagnosis of PAS and SMV-S contrary to the Sag TSE-T2W sequence.

COVID-CGAN: Efficient Deep Learning Approach for COVID-19 Detection Based on CXR Images Using Conditional GANs

COVID-19, a novel coronavirus infectious disease, has spread around the world, resulting in a large number of deaths. Due to a lack of physicians, emergency facilities, and equipment, medical systems have been unable to treat all patients in many countries. Deep learning is a promising approach for providing solutions to COVID-19 based on patients’ medical images. As COVID-19 is a new disease, its related dataset is still being collected and published. Small COVID-19 datasets may not be sufficient to build powerful deep learning detection models. Such models are often over-fitted, and their prediction results cannot be generalized. To fill this gap, we propose a deep learning approach for accurately detecting COVID-19 cases based on chest X-ray (CXR) images. For the proposed approach, named COVID-CGAN, we first generated a larger dataset using generative adversarial networks (GANs). Specifically, a customized conditional GAN (CGAN) was designed to generate the target COVID-19 CXR images. The expanded dataset, which contains 84.8% generated images and 15.2% original images, was then used for training five deep detection models: InceptionResNetV2, Xception, SqueezeNet, VGG16, and AlexNet. The results show that the use of the synthetic CXR images, which were generated by the customized CGAN, helped all deep learning models to achieve high detection accuracies. In particular, the highest accuracy was achieved by the InceptionResNetV2 model, which was 99.72% accurate with only ten epochs. All five models achieved kappa coefficients between 0.81 and 1, which is interpreted as an almost perfect agreement between the actual labels and the detected labels. Furthermore, the experiment showed that some models were faster yet smaller compared to the others but could still achieve high accuracy. For instance, SqueezeNet, which is a small network, required only three minutes and achieved comparable accuracy to larger networks such as InceptionResNetV2, which needed about 143 min. Our proposed approach can be applied to other fields with scarce datasets.

Modeling a deep transfer learning framework for the classification of COVID-19 radiology dataset

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-Coronavirus-2 or SARS-CoV-2), which came into existence in 2019, is a viral pandemic that caused coronavirus disease 2019 (COVID-19) illnesses and death. Research showed that relentless efforts had been made to improve key performance indicators for detection, isolation, and early treatment. This paper used Deep Transfer Learning Model (DTL) for the classification of a real-life COVID-19 dataset of chest X-ray images in both binary (COVID-19 or Normal) and three-class (COVID-19, Viral-Pneumonia or Normal) classification scenarios. Four experiments were performed where fine-tuned VGG-16 and VGG-19 Convolutional Neural Networks (CNNs) with DTL were trained on both binary and three-class datasets that contain X-ray images. The system was trained with an X-ray image dataset for the detection of COVID-19. The fine-tuned VGG-16 and VGG-19 DTL were modelled by employing a batch size of 10 in 40 epochs, Adam optimizer for weight updates, and categorical cross-entropy loss function. The results showed that the fine-tuned VGG-16 and VGG-19 models produced an accuracy of 99.23% and 98.00%, respectively, in the binary task. In contrast, in the multiclass (three-class) task, the fine-tuned VGG-16 and VGG-19 DTL models produced an accuracy of 93.85% and 92.92%, respectively. Moreover, the fine-tuned VGG-16 and VGG-19 models have MCC of 0.98 and 0.96 respectively in the binary classification, and 0.91 and 0.89 for multiclass classification. These results showed strong positive correlations between the models' predictions and the true labels. In the two classification tasks (binary and three-class), it was observed that the fine-tuned VGG-16 DTL model had stronger positive correlations in the MCC metric than the fine-tuned VGG-19 DTL model. The VGG-16 DTL model has a Kappa value of 0.98 as against 0.96 for the VGG-19 DTL model in the binary classification task, while in the three-class classification problem, the VGG-16 DTL model has a Kappa value of 0.91 as against 0.89 for the VGG-19 DTL model. This result is in agreement with the trend observed in the MCC metric. Hence, it was discovered that the VGG-16 based DTL model classified COVID-19 better than the VGG-19 based DTL model. Using the best performing fine-tuned VGG-16 DTL model, tests were carried out on 470 unlabeled image dataset, which was not used in the model training and validation processes. The test accuracy obtained for the model was 98%. The proposed models provided accurate diagnostics for both the binary and multiclass classifications, outperforming other existing models in the literature in terms of accuracy, as shown in this work.

Medical imaging and computational image analysis in COVID-19 diagnosis: A review

Coronavirus disease (COVID-19) is an infectious disease caused by a newly discovered coronavirus. The disease presents with symptoms such as shortness of breath, fever, dry cough, and chronic fatigue, amongst others. The disease may be asymptomatic in some patients in the early stages, which can lead to increased transmission of the disease to others. This study attempts to review papers on the role of imaging and medical image computing in COVID-19 diagnosis. For this purpose, PubMed, Scopus and Google Scholar were searched to find related studies until the middle of 2021. The contribution of this study is four-fold: 1) to use as a tutorial of the field for both clinicians and technologists, 2) to comprehensively review the characteristics of COVID-19 as presented in medical images, 3) to examine automated artificial intelligence-based approaches for COVID-19 diagnosis, 4) to express the research limitations in this field and the methods used to overcome them. Using machine learning-based methods can diagnose the disease with high accuracy from medical images and reduce time, cost and error of diagnostic procedure. It is recommended to collect bulk imaging data from patients in the shortest possible time to improve the performance of COVID-19 automated diagnostic methods.

Learning-to-augment strategy using noisy and denoised data: Improving generalizability of deep CNN for the detection of COVID-19 in X-ray images

Chest X-ray images are used in deep convolutional neural networks for the detection of COVID-19, the greatest human challenge of the 21st century. Robustness to noise and improvement of generalization are the major challenges in designing these networks. In this paper, we introduce a strategy for data augmentation using the determination of the type and value of noise density to improve the robustness and generalization of deep CNNs for COVID-19 detection. Firstly, we present a learning-to-augment approach that generates new noisy variants of the original image data with optimized noise density. We apply a Bayesian optimization technique to control and choose the optimal noise type and its parameters. Secondly, we propose a novel data augmentation strategy, based on denoised X-ray images, that uses the distance between denoised and original pixels to generate new data. We develop an autoencoder model to create new data using denoised images corrupted by the Gaussian and impulse noise. A database of chest X-ray images, containing COVID-19 positive, healthy, and non-COVID pneumonia cases, is used to fine-tune the pre-trained networks (AlexNet, ShuffleNet, ResNet18, and GoogleNet). The proposed method performs better results compared to the state-of-the-art learning to augment strategies in terms of sensitivity (0.808), specificity (0.915), and F-Measure (0.737). The source code of the proposed method is available at https://github.com/mohamadmomeny/Learning-to-augment-strategy.

Automatic COVID-19 Detection Using Exemplar Hybrid Deep Features with X-ray Images

COVID-19 and pneumonia detection using medical images is a topic of immense interest in medical and healthcare research. Various advanced medical imaging and machine learning techniques have been presented to detect these respiratory disorders accurately. In this work, we have proposed a novel COVID-19 detection system using an exemplar and hybrid fused deep feature generator with X-ray images. The proposed Exemplar COVID-19FclNet9 comprises three basic steps: exemplar deep feature generation, iterative feature selection and classification. The novelty of this work is the feature extraction using three pre-trained convolutional neural networks (CNNs) in the presented feature extraction phase. The common aspects of these pre-trained CNNs are that they have three fully connected layers, and these networks are AlexNet, VGG16 and VGG19. The fully connected layer of these networks is used to generate deep features using an exemplar structure, and a nine-feature generation method is obtained. The loss values of these feature extractors are computed, and the best three extractors are selected. The features of the top three fully connected features are merged. An iterative selector is used to select the most informative features. The chosen features are classified using a support vector machine (SVM) classifier. The proposed COVID-19FclNet9 applied nine deep feature extraction methods by using three deep networks together. The most appropriate deep feature generation model selection and iterative feature selection have been employed to utilise their advantages together. By using these techniques, the image classification ability of the used three deep networks has been improved. The presented model is developed using four X-ray image corpora (DB1, DB2, DB3 and DB4) with two, three and four classes. The proposed Exemplar COVID-19FclNet9 achieved a classification accuracy of 97.60%, 89.96%, 98.84% and 99.64% using the SVM classifier with 10-fold cross-validation for four datasets, respectively. Our developed Exemplar COVID-19FclNet9 model has achieved high classification accuracy for all four databases and may be deployed for clinical application.

COVID-Nets: deep CNN architectures for detecting COVID-19 using chest CT scans

In this paper we propose two novel deep convolutional network architectures, CovidResNet and CovidDenseNet, to diagnose COVID-19 based on CT images. The models enable transfer learning between different architectures, which might significantly boost the diagnostic performance. Whereas novel architectures usually suffer from the lack of pretrained weights, our proposed models can be partly initialized with larger baseline models like ResNet50 and DenseNet121, which is attractive because of the abundance of public repositories. The architectures are utilized in a first experimental study on the SARS-CoV-2 CT-scan dataset, which contains 4173 CT images for 210 subjects structured in a subject-wise manner into three different classes. The models differentiate between COVID-19, non-COVID-19 viral pneumonia, and healthy samples. We also investigate their performance under three binary classification scenarios where we distinguish COVID-19 from healthy, COVID-19 from non-COVID-19 viral pneumonia, and non-COVID-19 from healthy, respectively. Our proposed models achieve up to 93.87% accuracy, 99.13% precision, 92.49% sensitivity, 97.73% specificity, 95.70% F1-score, and 96.80% AUC score for binary classification, and up to 83.89% accuracy, 80.36% precision, 82.04% sensitivity, 92.07% specificity, 81.05% F1-score, and 94.20% AUC score for the three-class classification tasks. We also validated our models on the COVID19-CT dataset to differentiate COVID-19 and other non-COVID-19 viral infections, and our CovidDenseNet model achieved the best performance with 81.77% accuracy, 79.05% precision, 84.69% sensitivity, 79.05% specificity, 81.77% F1-score, and 87.50% AUC score. The experimental results reveal the effectiveness of the proposed networks in automated COVID-19 detection where they outperform standard models on the considered datasets while being more efficient.

A CNN-based scheme for COVID-19 detection with emergency services provisions using an optimal path planning

Unmanned Air Vehicles (UAVs) are becoming popular in real-world scenarios due to current advances in sensor technology and hardware platform development. The applications of UAVs in the medical field are broad and may be shared worldwide. With the recent outbreak of COVID-19, fast diagnostic testing has become one of the challenges due to the lack of test kits. UAVs can help in tackling the COVID-19 by delivering medication to the hospital on time. In this paper, to detect the number of COVID-19 cases in a hospital, we propose a deep convolution neural architecture using transfer learning, classifying the patient into three categories as COVID-19 (positive) and normal (negative), and pneumonia based on given X-ray images. The proposed deep-learning architecture is compared with state-of-the-art models. The results show that the proposed model provides an accuracy of 94.92%. Further to offer time-bounded services to COVID-19 patients, we have proposed a scheme for delivering emergency kits to the hospitals in need using an optimal path planning approach for UAVs in the network.

Intelligent predictions of Covid disease based on lung CT images using machine learning strategy

Covid or Corona Virus, a term ruling the world from past two years and causes a huge destruction in all countries. One of the most important Covid disease identification method is Lung based Computed Tomography (CT) image scanning, in which it provides an effective disease identification means in clear manner. However, this Lung CT image based disease detection principles are complex to health care representatives and doctors to predict the Covid disease accurately. Several manual errors and medical flaws are raised day-by-day, so that a new systematic methodology is required to identify the Covid disease effectively with respect to machine learning principles. The machine learning principles are most popular to identify the respective disease efficiently as well as classify the disease in accurate manner without any time consumption. The infected portions of the chest are identified accurately and report to the respective person without any delay. In this paper, a new machine learning strategy is introduced called Hybrid Disease Detection Principle (HDDP), in which it is derived from the two classical machine learning algorithms called Convolutional Neural Network (CNN) and the AdaBoost Classifier. Both these algorithms are integrated together to produce a new strategy called HDDP, in which it process the lung CT image based on the machine learning factors such as pre-processing, feature extraction and classification. Based on these effective image processing strategies the proposed algorithm handles the CT images to predict the Covid disease and report to the respective user with proper accuracy ratio. This paper intends to provide effcient disease predictions as well as provide a sufficient support to medical people and patients in fine manner to assist them with modern classification algorithms.

Federated Semi-Supervised Multi-Task Learning to Detect COVID-19 and Lungs Segmentation Marking Using Chest Radiography Images and Raspberry Pi Devices: An Internet of Medical Things Application

Internet of Medical Things (IoMT) provides an excellent opportunity to investigate better automatic medical decision support tools with the effective integration of various medical equipment and associated data. This study explores two such medical decision-making tasks, namely COVID-19 detection and lung area segmentation detection, using chest radiography images. We also explore different cutting-edge machine learning techniques, such as federated learning, semi-supervised learning, transfer learning, and multi-task learning to explore the issue. To analyze the applicability of computationally less capable edge devices in the IoMT system, we report the results using Raspberry Pi devices as accuracy, precision, recall, Fscore for COVID-19 detection, and average dice score for lung segmentation detection tasks. We also publish the results obtained through server-centric simulation for comparison. The results show that Raspberry Pi-centric devices provide better performance in lung segmentation detection, and server-centric experiments provide better results in COVID-19 detection. We also discuss the IoMT application-centric settings, utilizing medical data and decision support systems, and posit that such a system could benefit all the stakeholders in the IoMT domain.

Detail-oriented capsule network for classification of CT scan images performing the detection of COVID-19

COVID-19 is one of the biggest pandemics that the world is facing today, and every day, we are coming up with new challenges in this area. Still, much research is already going on to overcome this pandemic, and we also get succeeded to some extent. Diverse sources such as MRI, CT scanning, blood samples, X-ray image, and many more are available to detect COVID-19. Thus, it can be easily said that through image processing, the classification of COVID-19 can be done. In this study, the COVID-19 detection is done by classifying with the use of a type of convolutional neural network termed a detail-oriented capsule network. Chest CT scan imaging for the prediction of COVID-19 and non-COVID-19 are classified in the present paper using a Detailed Oriented capsule network (DOCN). Accuracy, specificity, and sensitivity are parameters used for model evaluation. The proposed model has achieved 98% accuracy, 81% sensitivity, and 98.4% specificity.

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

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.

CO-ResNet: Optimized ResNet model for COVID-19 diagnosis from X-ray images

This paper focuses on the application of deep learning (DL) based model in the analysis of novel coronavirus disease (COVID-19) from X-ray images. The novelty of this work is in the development of a new DL algorithm termed as optimized residual network (CO-ResNet) for COVID-19. The proposed CO-ResNet is developed by applying hyperparameter tuning to the conventional ResNet 101. CO-ResNet is applied to a novel dataset of 5,935 X-ray images retrieved from two publicly available datasets. By utilizing resizing, augmentation and normalization and testing different epochs our CO-ResNet was optimized for detecting COVID-19 versus pneumonia with normal healthy lung controls. Different evaluation metrics such as the classification accuracy, F1 score, recall, precision, area under the receiver operating characteristics curve (AUC) are used. Our proposed CO-ResNet obtains consistently best performance in the multi-level data classification problem, including health lung, pneumonia affected lung and COVID-19 affected lung samples. In the experimental evaluation, the detection rate accuracy in discerning COVID-19 is 98.74%, and for healthy normal lungs, pneumonia affected lungs are 92.08% and 91.32% respectively for our CO-ResNet with ResNet101 backbone. Further, our model obtained accuracy values of 83.68% and 82% for healthy normal lungs and pneumonia affected lungs with ResNet152 backbone. Experimental results indicate the potential usage of our new DL driven model for classification of COVID-19 and pneumonia.

A systematic review on AI/ML approaches against COVID-19 outbreak

A pandemic disease, COVID-19, has caused trouble worldwide by infecting millions of people. The studies that apply artificial intelligence (AI) and machine learning (ML) methods for various purposes against the COVID-19 outbreak have increased because of their significant advantages. Although AI/ML applications provide satisfactory solutions to COVID-19 disease, these solutions can have a wide diversity. This increase in the number of AI/ML studies and diversity in solutions can confuse deciding which AI/ML technique is suitable for which COVID-19 purposes. Because there is no comprehensive review study, this study systematically analyzes and summarizes related studies. A research methodology has been proposed to conduct the systematic literature review for framing the research questions, searching criteria and relevant data extraction. Finally, 264 studies were taken into account after following inclusion and exclusion criteria. This research can be regarded as a key element for epidemic and transmission prediction, diagnosis and detection, and drug/vaccine development. Six research questions are explored with 50 AI/ML approaches in COVID-19, 8 AI/ML methods for patient outcome prediction, 14 AI/ML techniques in disease predictions, along with five AI/ML methods for risk assessment of COVID-19. It also covers AI/ML method in drug development, vaccines for COVID-19, models in COVID-19, datasets and their usage and dataset applications with AI/ML.

Depth-wise dense neural network for automatic COVID19 infection detection and diagnosis

Coronavirus (COVID-19) and its new strain resulted in massive damage to society and brought panic worldwide. Automated medical image analysis such as X-rays, CT, and MRI offers excellent early diagnosis potential to augment the traditional healthcare strategy to fight against COVID-19. However, the identification of COVID infected lungs X-rays is challenging due to the high variation in infection characteristics and low-intensity contrast between normal tissues and infections. To identify the infected area, in this work, we present a novel depth-wise dense network that uniformly scales all dimensions and performs multilevel feature embedding, resulting in increased feature representations. The inclusion of depth wise component and squeeze-and-excitation results in better performance by capturing a more receptive field than the traditional convolutional layer; however, the parameters are almost the same. To improve the performance and training set, we have combined three large scale datasets. The extensive experiments on the benchmark X-rays datasets demonstrate the effectiveness of the proposed framework by achieving 96.17% in comparison to cutting-edge methods primarily based on transfer learning.

Automatic Quality Control in Lung X-Ray Imaging with Deep Learning

The development of deep learning and its growing application in medical diagnosis have focused the attention on automatic control of image quality for neural-network medical image analysis algorithms. This article presents a method for automatic determination of the hardness (penetration) of lung X-ray images using standard criteria from chest X-ray diagnosis. The proposed method can be applied to automatically filter images by hardness (penetration) level and to detect low-quality images, thus facilitating the creation of high-quality data sets and increasing the efficiency of neural-network approaches to the analysis of lung X-ray images.

LBP-based information assisted intelligent system for COVID-19 identification

A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.

Classification of COVID-19 chest X-Ray and CT images using a type of dynamic CNN modification method

Understanding and classifying Chest X-Ray (CXR) and computerised tomography (CT) images are of great significance for COVID-19 diagnosis. The existing research on the classification for COVID-19 cases faces the challenges of data imbalance, insufficient generalisability, the lack of comparative study, etc. To address these problems, this paper proposes a type of modified MobileNet to classify COVID-19 CXR images and a modified ResNet architecture for CT image classification. In particular, a modification method of convolutional neural networks (CNN) is designed to solve the gradient vanishing problem and improve the classification performance through dynamically combining features in different layers of a CNN. The modified MobileNet is applied to the classification of COVID-19, Tuberculosis, viral pneumonia (with the exception of COVID-19), bacterial pneumonia and normal controls using CXR images. Also, the proposed modified ResNet is used for the classification of COVID-19, non-COVID-19 infections and normal controls using CT images. The results show that the proposed methods achieve 99.6% test accuracy on the five-category CXR image dataset and 99.3% test accuracy on the CT image dataset. Six advanced CNN architectures and two specific COVID-19 detection models, i.e., COVID-Net and COVIDNet-CT are used in comparative studies. Two benchmark datasets and a CXR image dataset which combines eight different CXR image sources are employed to evaluate the performance of the above models. The results show that the proposed methods outperform the comparative models in classification accuracy, sensitivity, and precision, which demonstrate their potential in computer-aided diagnosis for healthcare applications.

A stacked ensemble for the detection of COVID-19 with high recall and accuracy

The main challenges for the automatic detection of the coronavirus disease (COVID-19) from computed tomography (CT) scans of an individual are: a lack of large datasets, ambiguity in the characteristics of COVID-19 and the detection techniques having low sensitivity (or recall). Hence, developing diagnostic techniques with high recall and automatic feature extraction using the available data are crucial for controlling the spread of COVID-19. This paper proposes a novel stacked ensemble capable of detecting COVID-19 from a patient's chest CT scans with high recall and accuracy. A systematic approach for designing a stacked ensemble from pre-trained computer vision models using transfer learning (TL) is presented. A novel diversity measure that results in the stacked ensemble with high recall and accuracy is proposed. The stacked ensemble proposed in this paper considers four pre-trained computer vision models: the visual geometry group (VGG)-19, residual network (ResNet)-101, densely connected convolutional network (DenseNet)-169 and wide residual network (WideResNet)-50-2. The proposed model was trained and evaluated with three different chest CT scans. As recall is more important than precision, the trade-offs between recall and precision were explored in relevance to COVID-19. The optimal recommended threshold values were found for each dataset.

Comprehensive Survey of Using Machine Learning in the COVID-19 Pandemic

Since December 2019, the global health population has faced the rapid spreading of coronavirus disease (COVID-19). With the incremental acceleration of the number of infected cases, the World Health Organization (WHO) has reported COVID-19 as an epidemic that puts a heavy burden on healthcare sectors in almost every country. The potential of artificial intelligence (AI) in this context is difficult to ignore. AI companies have been racing to develop innovative tools that contribute to arm the world against this pandemic and minimize the disruption that it may cause. The main objective of this study is to survey the decisive role of AI as a technology used to fight against the COVID-19 pandemic. Five significant applications of AI for COVID-19 were found, including (1) COVID-19 diagnosis using various data types (e.g., images, sound, and text); (2) estimation of the possible future spread of the disease based on the current confirmed cases; (3) association between COVID-19 infection and patient characteristics; (4) vaccine development and drug interaction; and (5) development of supporting applications. This study also introduces a comparison between current COVID-19 datasets. Based on the limitations of the current literature, this review highlights the open research challenges that could inspire the future application of AI in COVID-19.

Detecting COVID-19 in Chest X-Ray Images via MCFF-Net

COVID-19 is a respiratory disease caused by severe acute respiratory syndrome coronavirus (SARS-CoV-2). Due to the rapid spread of COVID-19 around the world, the number of COVID-19 cases continues to increase, and lots of countries are facing tremendous pressure on both public and medical resources. Although RT-PCR is the most widely used detection technology with COVID-19 detection, it still has some limitations, such as high cost, being time-consuming, and having low sensitivity. According to the characteristics of chest X-ray (CXR) images, we design the Parallel Channel Attention Feature Fusion Module (PCAF), as well as a new structure of convolutional neural network MCFF-Net proposed based on PCAF. In order to improve the recognition efficiency, the network adopts 3 classifiers: 1-FC, GAP-FC, and Conv1-GAP. The experimental results show that the overall accuracy of MCFF-Net66-Conv1-GAP model is 94.66% for 4-class classification. Simultaneously, the classification accuracy, precision, sensitivity, specificity, and F1-score of COVID-19 are 100%. MCFF-Net may not only assist clinicians in making appropriate decisions for COVID-19 diagnosis but also mitigate the lack of testing kits.

Enhance Portable Radiograph for Fast and High Accurate COVID-19 Monitoring

This work aimed to assist physicians by improving their speed and diagnostic accuracy when interpreting portable CXRs as well as monitoring the treatment process to see whether a patient is improving or deteriorating with treatment. These objectives are in especially high demand in the setting of the ongoing COVID-19 pandemic. With the recent progress in the development of artificial intelligence (AI), we introduce new deep learning frameworks to align and enhance the quality of portable CXRs to be more consistent, and to more closely match higher quality conventional CXRs. These enhanced portable CXRs can then help the doctors provide faster and more accurate diagnosis and treatment planning. The contributions of this work are four-fold. Firstly, a new database collection of subject-pair radiographs is introduced. For each subject, we collected a pair of samples from both portable and conventional machines. Secondly, a new deep learning approach is presented to align the subject-pairs dataset to obtain a pixel-pairs dataset. Thirdly, a new PairFlow approach is presented, an end-to-end invertible transfer deep learning method, to enhance the degraded quality of portable CXRs. Finally, the performance of the proposed system is evaluated by UAMS doctors in terms of both image quality and topological properties. This work was undertaken in collaboration with the Department of Radiology at the University of Arkansas for Medical Sciences (UAMS) to enhance portable/mobile COVID-19 CXRs, to improve the speed and accuracy of portable CXR images and aid in urgent COVID-19 diagnosis, monitoring and treatment.

Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking

One of the promising methods for early detection of Coronavirus Disease 2019 (COVID-19) among symptomatic patients is to analyze chest Computed Tomography (CT) scans or chest x-rays images of individuals using Deep Learning (DL) techniques. This paper proposes a novel stacked ensemble to detect COVID-19 either from chest CT scans or chest x-ray images of an individual. The proposed model is a stacked ensemble of heterogenous pre-trained computer vision models. Four pre-trained DL models were considered: Visual Geometry Group (VGG 19), Residual Network (ResNet 101), Densely Connected Convolutional Networks (DenseNet 169) and Wide Residual Network (WideResNet 50 2). From each pre-trained model, the potential candidates for base classifiers were obtained by varying the number of additional fully-connected layers. After an exhaustive search, three best-performing diverse models were selected to design a weighted average-based heterogeneous stacked ensemble. Five different chest CT scans and chest x-ray images were used to train and evaluate the proposed model. The performance of the proposed model was compared with two other ensemble models, baseline pre-trained computer vision models and existing models for COVID-19 detection. The proposed model achieved uniformly good performance on five different datasets, consisting of chest CT scans and chest x-rays images. In relevance to COVID-19, as the recall is more important than precision, the trade-offs between recall and precision at different thresholds were explored. Recommended threshold values which yielded a high recall and accuracy were obtained for each dataset.

An Overview of Deep Learning Techniques on Chest X-Ray and CT Scan Identification of COVID-19

Pneumonia is an infamous life-threatening lung bacterial or viral infection. The latest viral infection endangering the lives of many people worldwide is the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which causes COVID-19. This paper is aimed at detecting and differentiating viral pneumonia and COVID-19 disease using digital X-ray images. The current practices include tedious conventional processes that solely rely on the radiologist or medical consultant's technical expertise that are limited, time-consuming, inefficient, and outdated. The implementation is easily prone to human errors of being misdiagnosed. The development of deep learning and technology improvement allows medical scientists and researchers to venture into various neural networks and algorithms to develop applications, tools, and instruments that can further support medical radiologists. This paper presents an overview of deep learning techniques made in the chest radiography on COVID-19 and pneumonia cases.

Integrating Digital Technologies and Public Health to Fight Covid-19 Pandemic: Key Technologies, Applications, Challenges and Outlook of Digital Healthcare

Integration of digital technologies and public health (or digital healthcare) helps us to fight the Coronavirus Disease 2019 (COVID-19) pandemic, which is the biggest public health crisis humanity has faced since the 1918 Influenza Pandemic. In order to better understand the digital healthcare, this work conducted a systematic and comprehensive review of digital healthcare, with the purpose of helping us combat the COVID-19 pandemic. This paper covers the background information and research overview of digital healthcare, summarizes its applications and challenges in the COVID-19 pandemic, and finally puts forward the prospects of digital healthcare. First, main concepts, key development processes, and common application scenarios of integrating digital technologies and digital healthcare were offered in the part of background information. Second, the bibliometric techniques were used to analyze the research output, geographic distribution, discipline distribution, collaboration network, and hot topics of digital healthcare before and after COVID-19 pandemic. We found that the COVID-19 pandemic has greatly accelerated research on the integration of digital technologies and healthcare. Third, application cases of China, EU and U.S using digital technologies to fight the COVID-19 pandemic were collected and analyzed. Among these digital technologies, big data, artificial intelligence, cloud computing, 5G are most effective weapons to combat the COVID-19 pandemic. Applications cases show that these technologies play an irreplaceable role in controlling the spread of the COVID-19. By comparing the application cases in these three regions, we contend that the key to China's success in avoiding the second wave of COVID-19 pandemic is to integrate digital technologies and public health on a large scale without hesitation. Fourth, the application challenges of digital technologies in the public health field are summarized. These challenges mainly come from four aspects: data delays, data fragmentation, privacy security, and data security vulnerabilities. Finally, this study provides the future application prospects of digital healthcare. In addition, we also provide policy recommendations for other countries that use digital technology to combat COVID-19.

Automated Detection of COVID-19 Cases on Radiographs using Shape-Dependent Fibonacci-p Patterns

The coronavirus (COVID-19) pandemic has been adversely affecting people's health globally. To diminish the effect of this widespread pandemic, it is essential to detect COVID-19 cases as quickly as possible. Chest radiographs are less expensive and are a widely available imaging modality for detecting chest pathology compared with CT images. They play a vital role in early prediction and developing treatment plans for suspected or confirmed COVID-19 chest infection patients. In this paper, a novel shape-dependent Fibonacci-p patterns-based feature descriptor using a machine learning approach is proposed. Computer simulations show that the presented system (1) increases the effectiveness of differentiating COVID-19, viral pneumonia, and normal conditions, (2) is effective on small datasets, and (3) has faster inference time compared to deep learning methods with comparable performance. Computer simulations are performed on two publicly available datasets; (a) the Kaggle dataset, and (b) the COVIDGR dataset. To assess the performance of the presented system, various evaluation parameters, such as accuracy, recall, specificity, precision, and f1-score are used. Nearly 100% differentiation between normal and COVID-19 radiographs is observed for the three-class classification scheme using the lung area-specific Kaggle radiographs. While Recall of 72.65 ± 6.83 and specificity of 77.72 ± 8.06 is observed for the COVIDGR dataset.

A Deep-Learning Diagnostic Support System for the Detection of COVID-19 Using Chest Radiographs: A Multireader Validation Study

MATERIALS AND METHODS

Five publicly available databases comprising normal CXR, confirmed COVID-19 pneumonia cases, and other pneumonias were used. After the harmonization of the data, the training set included 7966 normal cases, 5451 with other pneumonia, and 258 CXRs with COVID-19 pneumonia, whereas in the testing data set, each category was represented by 100 cases. Eleven blinded radiologists with various levels of expertise independently read the testing data set. The data were analyzed separately with the newly proposed artificial intelligence-based system and by consultant radiologists and residents, with respect to positive predictive value (PPV), sensitivity, and F-score (harmonic mean for PPV and sensitivity). The χ2 test was used to compare the sensitivity, specificity, accuracy, PPV, and F-scores of the readers and the system.

RESULTS

The proposed system achieved higher overall diagnostic accuracy (94.3%) than the radiologists (61.4% ± 5.3%). The radiologists reached average sensitivities for normal CXR, other type of pneumonia, and COVID-19 pneumonia of 85.0% ± 12.8%, 60.1% ± 12.2%, and 53.2% ± 11.2%, respectively, which were significantly lower than the results achieved by the algorithm (98.0%, 88.0%, and 97.0%; P < 0.00032). The mean PPVs for all 11 radiologists for the 3 categories were 82.4%, 59.0%, and 59.0% for the healthy, other pneumonia, and COVID-19 pneumonia, respectively, resulting in an F-score of 65.5% ± 12.4%, which was significantly lower than the F-score of the algorithm (94.3% ± 2.0%, P < 0.00001). When other pneumonia and COVID-19 pneumonia cases were pooled, the proposed system reached an accuracy of 95.7% for any pathology and the radiologists, 88.8%. The overall accuracy of consultants did not vary significantly compared with residents (65.0% ± 5.8% vs 67.4% ± 4.2%); however, consultants detected significantly more COVID-19 pneumonia cases (P = 0.008) and less healthy cases (P < 0.00001).

CONCLUSIONS

The system showed robust accuracy for COVID-19 pneumonia detection on CXR and surpassed radiologists at various training levels.

COVID-19 disease severity assessment using CNN model

Due to the highly infectious nature of the novel coronavirus (COVID-19) disease, excessive number of patients waits in the line for chest X-ray examination, which overloads the clinicians and radiologists and negatively affects the patient's treatment, prognosis and control of the pandemic. Now that the clinical facilities such as the intensive care units and the mechanical ventilators are very limited in the face of this highly contagious disease, it becomes quite important to classify the patients according to their severity levels. This paper presents a novel implementation of convolutional neural network (CNN) approach for COVID-19 disease severity classification (assessment). An automated CNN model is designed and proposed to divide COVID-19 patients into four severity classes as mild, moderate, severe, and critical with an average accuracy of 95.52% using chest X-ray images as input. Experimental results on a sufficiently large number of chest X-ray images demonstrate the effectiveness of CNN model produced with the proposed framework. To the best of the author's knowledge, this is the first COVID-19 disease severity assessment study with four stages (mild vs. moderate vs. severe vs. critical) using a sufficiently large number of X-ray images dataset and CNN whose almost all hyper-parameters are automatically tuned by the grid search optimiser.

COVID-19 in the Age of Artificial Intelligence: A Comprehensive Review

The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.

Diagnosis of corona diseases from associated genes and X-ray images using machine learning algorithms and deep CNN

Novel Coronavirus with its highly transmittable characteristics is rapidly spreading, endangering millions of human lives and the global economy. To expel the chain of alteration and subversive expansion, early and effective diagnosis of infected patients is immensely important. Unfortunately, there is a lack of testing equipment in many countries as compared with the number of infected patients. It would be desirable to have a swift diagnosis with identification of COVID-19 from disease genes or from CT or X-Ray images. COVID-19 causes flus, cough, pneumonia, and lung infection in patients, wherein massive alveolar damage and progressive respiratory failure can lead to death. This paper proposes two different detection methods - the first is a Gene-based screening method to detect Corona diseases (Middle East respiratory syndrome-related coronavirus, Severe acute respiratory syndrome coronavirus 2, and Human coronavirus HKU1) and differentiate it from Pneumonia. This novel approach to healthcare utilizes disease genes to build functional semantic similarity among genes. Different machine learning algorithms - eXtreme Gradient Boosting, Naïve Bayes, Regularized Random Forest, Random Forest Rule-Based Model, Random Ferns, C5.0 and Multi-Layer Perceptron, are trained and tested on the semantic similarities to classify Corona and Pneumonia diseases. The best performing models are then ensembled, yielding an accuracy of nearly 93%. The second diagnosis technique proposed herein is an automated COVID-19 diagnostic method which uses chest X-ray images to classify Normal versus COVID-19 and Pneumonia versus COVID-19 images using the deep-CNN technique, achieving 99.87% and 99.48% test accuracy. Thus, this research can be an assistance for providing better treatment against COVID-19.

Artificial Intelligence and Medical Internet of Things Framework for Diagnosis of Coronavirus Suspected Cases

The world has been facing the COVID-19 pandemic since December 2019. Timely and efficient diagnosis of COVID-19 suspected patients plays a significant role in medical treatment. The deep transfer learning-based automated COVID-19 diagnosis on chest X-ray is required to counter the COVID-19 outbreak. This work proposes a real-time Internet of Things (IoT) framework for early diagnosis of suspected COVID-19 patients by using ensemble deep transfer learning. The proposed framework offers real-time communication and diagnosis of COVID-19 suspected cases. The proposed IoT framework ensembles four deep learning models such as InceptionResNetV2, ResNet152V2, VGG16, and DenseNet201. The medical sensors are utilized to obtain the chest X-ray modalities and diagnose the infection by using the deep ensemble model stored on the cloud server. The proposed deep ensemble model is compared with six well-known transfer learning models over the chest X-ray dataset. Comparative analysis revealed that the proposed model can help radiologists to efficiently and timely diagnose the COVID-19 suspected patients.

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

BACKGROUND

Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task.

METHODOLOGY

This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models.

RESULTS

In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences.

CONCLUSIONS

The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

CovFrameNet: An Enhanced Deep Learning Framework for COVID-19 Detection

The novel coronavirus, also known as COVID-19, is a pandemic that has weighed heavily on the socio-economic affairs of the world. Research into the production of relevant vaccines is progressively being advanced with the development of the Pfizer and BioNTech, AstraZeneca, Moderna, Sputnik V, Janssen, Sinopharm, Valneva, Novavax and Sanofi Pasteur vaccines. There is, however, a need for a computational intelligence solution approach to mediate the process of facilitating quick detection of the disease. Different computational intelligence methods, which comprise natural language processing, knowledge engineering, and deep learning, have been proposed in the literature to tackle the spread of coronavirus disease. More so, the application of deep learning models have demonstrated an impressive performance compared to other methods. This paper aims to advance the application of deep learning and image pre-processing techniques to characterise and detect novel coronavirus infection. Furthermore, the study proposes a framework named CovFrameNet., which consist of a pipelined image pre-processing method and a deep learning model for feature extraction, classification, and performance measurement. The novelty of this study lies in the design of a CNN architecture that incorporates an enhanced image pre-processing mechanism. The National Institutes of Health (NIH) Chest X-Ray dataset and COVID-19 Radiography database were used to evaluate and validate the effectiveness of the proposed deep learning model. Results obtained revealed that the proposed model achieved an accuracy of 0.1, recall/precision of 0.85, F-measure of 0.9, and specificity of 1.0. Thus, the study's outcome showed that a CNN-based method with image pre-processing capability could be adopted for the pre-screening of suspected COVID-19 cases, and the confirmation of RT-PCR-based detected cases of COVID-19.

Automated COVID-19 Detection from Chest X-Ray Images: A High-Resolution Network (HRNet) Approach

The pandemic, originated by novel coronavirus 2019 (COVID-19), continuing its devastating effect on the health, well-being, and economy of the global population. A critical step to restrain this pandemic is the early detection of COVID-19 in the human body to constraint the exposure and control the spread of the virus. Chest X-Rays are one of the non-invasive tools to detect this disease as the manual PCR diagnosis process is quite tedious and time-consuming. Our intensive background studies show that, the works till now are not efficient to produce an unbiased detection result. In this work, we proposed an automated COVID-19 classification method, utilizing available COVID and non-COVID X-Ray datasets, along with High-Resolution Network (HRNet) for feature extraction embedding with the UNet for segmentation purposes. To evaluate the proposed method, several baseline experiments have been performed employing numerous deep learning architectures. With extensive experiment, we got a significant result of 99.26% accuracy, 98.53% sensitivity, and 98.82% specificity with HRNet which surpasses the performances of the existing models. Finally, we conclude that our proposed methodology ensures unbiased high accuracy, which increases the probability of incorporating X-Ray images into the diagnosis of the disease.

Automatic and Reliable Leaf Disease Detection Using Deep Learning Techniques

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also overcome the shortcomings of continuous human monitoring. In this work, we propose the use of a deep learning architecture based on a recent convolutional neural network called EfficientNet on 18,161 plain and segmented tomato leaf images to classify tomato diseases. The performance of two segmentation models i.e., U-net and Modified U-net, for the segmentation of leaves is reported. The comparative performance of the models for binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. The modified U-net segmentation model showed accuracy, IoU, and Dice score of 98.66%, 98.5%, and 98.73%, respectively, for the segmentation of leaf images. EfficientNet-B7 showed superior performance for the binary classification and six-class classification using segmented images with an accuracy of 99.95% and 99.12%, respectively. Finally, EfficientNet-B4 achieved an accuracy of 99.89% for ten-class classification using segmented images. It can be concluded that all the architectures performed better in classifying the diseases when trained with deeper networks on segmented images. The performance of each of the experimental studies reported in this work outperforms the existing literature.

Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images

Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.