Automatic diagnosis of COVID-19 from CT images using CycleGAN and transfer learning.

The outbreak of the corona virus disease (COVID-19) has changed the lives of most people on Earth. Given the high prevalence of this disease, its correct diagnosis in order to quarantine patients is of the utmost importance in the steps of fighting this pandemic. Among the various modalities used for diagnosis, medical imaging, especially computed tomography (CT) imaging, has been the focus of many previous studies due to its accuracy and availability. In addition, automation of diagnostic methods can be of great help to physicians. In this paper, a method based on pre-trained deep neural networks is presented, which, by taking advantage of a cyclic generative adversarial net (CycleGAN) model for data augmentation, has reached state-of-the-art performance for the task at hand, i.e., 99.60% accuracy. Also, in order to evaluate the method, a dataset containing 3163 images from 189 patients has been collected and labeled by physicians. Unlike prior datasets, normal data have been collected from people suspected of having COVID-19 disease and not from data from other diseases, and this database is made available publicly. Moreover, the method's reliability is further evaluated by calibration metrics, and its decision is interpreted by Grad-CAM also to find suspicious regions as another output of the method and make its decisions trustworthy and explainable.

CTMLP: Can MLPs replace CNNs or transformers for COVID-19 diagnosis?

BACKGROUND: Convolutional Neural Networks (CNNs) and the hybrid models of CNNs and Vision Transformers (VITs) are the recent mainstream methods for COVID-19 medical image diagnosis. However, pure CNNs lack global modeling ability, and the hybrid models of CNNs and VITs have problems such as large parameters and computational complexity. These models are difficult to be used effectively for medical diagnosis in just-in-time applications. METHODS: Therefore, a lightweight medical diagnosis network CTMLP based on convolutions and multi-layer perceptrons (MLPs) is proposed for the diagnosis of COVID-19. The previous self-supervised algorithms are based on CNNs and VITs, and the effectiveness of such algorithms for MLPs is not yet known. At the same time, due to the lack of ImageNet-scale datasets in the medical image domain for model pre-training. So, a pre-training scheme TL-DeCo based on transfer learning and self-supervised learning was constructed. In addition, TL-DeCo is too tedious and resource-consuming to build a new model each time. Therefore, a guided self-supervised pre-training scheme was constructed for the new lightweight model pre-training. RESULTS: The proposed CTMLP achieves an accuracy of 97.51%, an f1-score of 97.43%, and a recall of 98.91% without pre-training, even with only 48% of the number of ResNet50 parameters. Furthermore, the proposed guided self-supervised learning scheme can improve the baseline of simple self-supervised learning by 1%-1.27%. CONCLUSION: The final results show that the proposed CTMLP can replace CNNs or Transformers for a more efficient diagnosis of COVID-19. In addition, the additional pre-training framework was developed to make it more promising in clinical practice.

ELUCNN for explainable COVID-19 diagnosis.

COVID-19 is a positive-sense single-stranded RNA virus caused by a strain of coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Several noteworthy variants of SARS-CoV-2 were declared by WHO as Alpha, Beta, Gamma, Delta, and Omicron. Till 13/Dec/2022, it has caused 6.65 million death tolls, and over 649 million confirmed positive cases. Based on the convolutional neural network (CNN), this study first proposes a ten-layer CNN as the backbone model. Then, the exponential linear unit (ELU) is introduced to replace ReLU, and the traditional convolutional block is now transformed into conv-ELU. Finally, an ELU-based CNN (ELUCNN) model is proposed for COVID-19 diagnosis. Besides, the MDA strategy is used to enhance the size of the training set. We develop a mobile app integrating ELUCNN, and this web app is run on a client-server modeled structure. Ten runs of the tenfold cross-validation experiment show our model yields a sensitivity of 94.41 ± 0.98 , a specificity of 94.84 ± 1.21 , an accuracy of 94.62 ± 0.96 , and an F1 score of 94.61 ± 0.95 . The ELUCNN model and mobile app are effective in COVID-19 diagnosis and give better results than 14 state-of-the-art COVID-19 diagnosis models concerning accuracy.

NSCGCN: A novel deep GCN model to diagnosis COVID-19.

AIM: Corona Virus Disease 2019 (COVID-19) was a lung disease with high mortality and was highly contagious. Early diagnosis of COVID-19 and distinguishing it from pneumonia was beneficial for subsequent treatment. OBJECTIVES: Recently, Graph Convolutional Network (GCN) has driven a significant contribution to disease diagnosis. However, limited by the nature of the graph convolution algorithm, deep GCN has an over-smoothing problem. Most of the current GCN models are shallow neural networks, which do not exceed five layers. Furthermore, the objective of this study is to develop a novel deep GCN model based on the DenseGCN and the pre-trained model of deep Convolutional Neural Network (CNN) to complete the diagnosis of chest X-ray (CXR) images. METHODS: We apply the pre-trained model of deep CNN to perform feature extraction on the data to complete the extraction of pixel-level features in the image. And then, to extract the potential relationship between the obtained features, we propose Neighbourhood Feature Reconstruction Algorithm to reconstruct them into graph-structured data. Finally, we design a deep GCN model that exploits the graph-structured data to diagnose COVID-19 effectively. In the deep GCN model, we propose a Node-Self Convolution Algorithm (NSC) based on feature fusion to construct a deep GCN model called NSCGCN (Node-Self Convolution Graph Convolutional Network). RESULTS: Experiments were carried out on the Computed Tomography (CT) and CXR datasets. The results on the CT dataset confirmed that: compared with the six state-of-the-art (SOTA) shallow GCN models, the accuracy and sensitivity of the proposed NSCGCN had improve 8% as sensitivity (Sen.) = 87.50%, F1 score = 97.37%, precision (Pre.) = 89.10%, accuracy (Acc.) = 97.50%, area under the ROC curve (AUC) = 97.09%. Moreover, the results on the CXR dataset confirmed that: compared with the fourteen SOTA GCN models, sixteen SOTA CNN transfer learning models and eight SOTA COVID-19 diagnosis methods on the COVID-19 dataset. Our proposed method had best performances as Sen. = 96.45%, F1 score = 96.45%, Pre. = 96.61%, Acc. = 96.45%, AUC = 99.22%. CONCLUSION: Our proposed NSCGCN model is effective and performed better than the thirty-eight SOTA methods. Thus, the proposed NSC could help build deep GCN models. Our proposed COVID-19 diagnosis method based on the NSCGCN model could help radiologists detect pneumonia from CXR images and distinguish COVID-19 from Ordinary Pneumonia (OPN). The source code of this work will be publicly available at https://github.com/TangChaosheng/NSCGCN.

AVNC: Attention-Based VGG-Style Network for COVID-19 Diagnosis by CBAM.

(Aim) To detect COVID-19 patients more accurately and more precisely, we proposed a novel artificial intelligence model. (Methods) We used previously proposed chest CT dataset containing four categories: COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis, and healthy subjects. First, we proposed a novel VGG-style base network (VSBN) as backbone network. Second, convolutional block attention module (CBAM) was introduced as attention module into our VSBN. Third, an improved multiple-way data augmentation method was used to resist overfitting of our AI model. In all, our model was dubbed as a 12-layer attention-based VGG-style network for COVID-19 (AVNC) (Results) This proposed AVNC achieved the sensitivity/precision/F1 per class all above 95%. Particularly, AVNC yielded a micro-averaged F1 score of 96.87%, which is higher than 11 state-of-the-art approaches. (Conclusion) This proposed AVNC is effective in recognizing COVID-19 diseases.

Epidemiological Characteristics of COVID-19 Outbreak in Yangzhou, China, 2021.

Objective: Epidemiological characteristics of COVID-19 outbreak in Yangzhou city caused by the highly contagious Delta variant strain of SARS-CoV-2 virus were investigated in this retrospective descriptive study to provide prevention and control guidelines for outbreaks in the future. Methods: All the epidemiological data used in this study were collected manually from the official website of the Yangzhou Municipal Health Committee from 28 July to 26 August 2021, and then were analyzed systematically and statistically in this study. Results: A total of 570 COVID-19 cases were reported during the short-term outbreak in Yangzhou City. The ages of infected individuals ranged from 1 to 90 years with the average age at 49.47 ± 22.69 years. As for gender distributions, the ratio of male- to-female patients was 1:1.36 (242:328). Geographic analysis showed that 377 patients (66.1%) were in Hanjiang District while 188 patients (33.0%) were in Guangling District. Clinical diagnosis showed that 175 people (30.7%) had mild symptoms, 385 people were in moderate conditions (67.5%), and 10 people were in severe situations (1.8%). Significant age differences were found among the three groups (P < 0.001). However, no significant difference was identified in terms of gender ratio (P > 0.05). Based on the transmission chain formed by 6 generations of infected persons with a clear transmission relationship, the age showed a gradually decreasing trend, while the median time of diagnosis in 2 adjacent generations was 3 days. In addition, the estimated basic reproduction number R 0 of the Delta variant was 3.3651 by the classical Susceptible, Infectious, and/or Recovered (SIR) model. Conclusion: The Delta variant of SARS-CoV-2 was highly infectious and has obvious clustering characteristics during the Yangzhou outbreak in China.

WVALE: Weak variational autoencoder for localisation and enhancement of COVID-19 lung infections.

BACKGROUND AND OBJECTIVE: The COVID-19 pandemic is a major global health crisis of this century. The use of neural networks with CT imaging can potentially improve clinicians' efficiency in diagnosis. Previous studies in this field have primarily focused on classifying the disease on CT images, while few studies targeted the localisation of disease regions. Developing neural networks for automating the latter task is impeded by limited CT images with pixel-level annotations available to the research community. METHODS: This paper proposes a weakly-supervised framework named "Weak Variational Autoencoder for Localisation and Enhancement" (WVALE) to address this challenge for COVID-19 CT images. This framework includes two components: anomaly localisation with a novel WVAE model and enhancement of supervised segmentation models with WVALE. RESULTS: The WVAE model have been shown to produce high-quality post-hoc attention maps with fine borders around infection regions, while weak supervision segmentation shows results comparable to conventional supervised segmentation models. The WVALE framework can enhance the performance of a range of supervised segmentation models, including state-of-art models for the segmentation of COVID-19 lung infection. CONCLUSIONS: Our study provides a proof-of-concept for weakly supervised segmentation and an alternative approach to alleviate the lack of annotation, while its independence from classification & segmentation frameworks makes it easily integrable with existing systems.

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning Architecture.

COVID-19 is a contagious infection that has severe effects on the global economy and our daily life. Accurate diagnosis of COVID-19 is of importance for consultants, patients, and radiologists. In this study, we use the deep learning network AlexNet as the backbone, and enhance it with the following two aspects: 1) adding batch normalization to help accelerate the training, reducing the internal covariance shift; 2) replacing the fully connected layer in AlexNet with three classifiers: SNN, ELM, and RVFL. Therefore, we have three novel models from the deep COVID network (DC-Net) framework, which are named DC-Net-S, DC-Net-E, and DC-Net-R, respectively. After comparison, we find the proposed DC-Net-R achieves an average accuracy of 90.91% on a private dataset (available upon email request) comprising of 296 images while the specificity reaches 96.13%, and has the best performance among all three proposed classifiers. In addition, we show that our DC-Net-R also performs much better than other existing algorithms in the literature. Supplementary Information: The online version contains supplementary material available at 10.1007/s11390-020-0679-8.

TSRNet: Diagnosis of COVID-19 based on self-supervised learning and hybrid ensemble model.

BACKGROUND: As of Feb 27, 2022, coronavirus (COVID-19) has caused 434,888,591 infections and 5,958,849 deaths worldwide, dealing a severe blow to the economies and cultures of most countries around the world. As the virus has mutated, its infectious capacity has further increased. Effective diagnosis of suspected cases is an important tool to stop the spread of the pandemic. Therefore, we intended to develop a computer-aided diagnosis system for the diagnosis of suspected cases. METHODS: To address the shortcomings of commonly used pre-training methods and exploit the information in unlabeled images, we proposed a new pre-training method based on transfer learning with self-supervised learning (TS). After that, a new convolutional neural network based on attention mechanism and deep residual network (RANet) was proposed to extract features. Based on this, a hybrid ensemble model (TSRNet) was proposed for classifying lung CT images of suspected patients as COVID-19 and normal. RESULTS: Compared with the existing five models in terms of accuracy (DarkCOVIDNet: 98.08%; Deep-COVID: 97.58%; NAGNN: 97.86%; COVID-ResNet: 97.78%; Patch-based CNN: 88.90%), TSRNet has the highest accuracy of 99.80%. In addition, the recall, f1-score, and AUC of the model reached 99.59%, 99.78%, and 1, respectively. CONCLUSION: TSRNet can effectively diagnose suspected COVID-19 cases with the help of the information in unlabeled and labeled images, thus helping physicians to adopt early treatment plans for confirmed cases.

EDNC: Ensemble Deep Neural Network for COVID-19 Recognition.

The automatic recognition of COVID-19 diseases is critical in the present pandemic since it relieves healthcare staff of the burden of screening for infection with COVID-19. Previous studies have proven that deep learning algorithms can be utilized to aid in the diagnosis of patients with potential COVID-19 infection. However, the accuracy of current COVID-19 recognition models is relatively low. Motivated by this fact, we propose three deep learning architectures, F-EDNC, FC-EDNC, and O-EDNC, to quickly and accurately detect COVID-19 infections from chest computed tomography (CT) images. Sixteen deep learning neural networks have been modified and trained to recognize COVID-19 patients using transfer learning and 2458 CT chest images. The proposed EDNC has then been developed using three of sixteen modified pre-trained models to improve the performance of COVID-19 recognition. The results suggested that the F-EDNC method significantly enhanced the recognition of COVID-19 infections with 97.75% accuracy, followed by FC-EDNC and O-EDNC (97.55% and 96.12%, respectively), which is superior to most of the current COVID-19 recognition models. Furthermore, a localhost web application has been built that enables users to easily upload their chest CT scans and obtain their COVID-19 results automatically. This accurate, fast, and automatic COVID-19 recognition system will relieve the stress of medical professionals for screening COVID-19 infections.

Covid-19 Diagnosis by WE-SAJ.

With a global COVID-19 pandemic, the number of confirmed patients increases rapidly, leaving the world with very few medical resources. Therefore, the fast diagnosis and monitoring of COVID-19 are one of the world's most critical challenges today. Artificial intelligence-based CT image classification models can quickly and accurately distinguish infected patients from healthy populations. Our research proposes a deep learning model (WE-SAJ) using wavelet entropy for feature extraction, two-layer FNNs for classification and the adaptive Jaya algorithm as a training algorithm. It achieves superior performance compared to the Jaya-based model. The model has a sensitivity of 85.47±1.84, specificity of 87.23±1.67 precision of 87.03±1.34, an accuracy of 86.35±0.70, and F1 score of 86.23±0.77, Matthews correlation coefficient of 72.75±1.38, and Fowlkes-Mallows Index of 86.24±0.76. Our experiments demonstrate the potential of artificial intelligence techniques for COVID-19 diagnosis and the effectiveness of the Self-adaptive Jaya algorithm compared to the Jaya algorithm for medical image classification tasks.

Two-Scale Multimodal Medical Image Fusion Based on Structure Preservation.

Medical image fusion has an indispensable value in the medical field. Taking advantage of structure-preserving filter and deep learning, a structure preservation-based two-scale multimodal medical image fusion algorithm is proposed. First, we used a two-scale decomposition method to decompose source images into base layer components and detail layer components. Second, we adopted a fusion method based on the iterative joint bilateral filter to fuse the base layer components. Third, a convolutional neural network and local similarity of images are used to fuse the components of the detail layer. At the last, the final fused result is got by using two-scale image reconstruction. The contrast experiments display that our algorithm has better fusion results than the state-of-the-art medical image fusion algorithms.

Tiled Sparse Coding in Eigenspaces for Image Classification.

The automation in the diagnosis of medical images is currently a challenging task. The use of Computer Aided Diagnosis (CAD) systems can be a powerful tool for clinicians, especially in situations when hospitals are overflowed. These tools are usually based on artificial intelligence (AI), a field that has been recently revolutionized by deep learning approaches. These alternatives usually obtain a large performance based on complex solutions, leading to a high computational cost and the need of having large databases. In this work, we propose a classification framework based on sparse coding. Images are first partitioned into different tiles, and a dictionary is built after applying PCA to these tiles. The original signals are then transformed as a linear combination of the elements of the dictionary. Then, they are reconstructed by iteratively deactivating the elements associated with each component. Classification is finally performed employing as features the subsequent reconstruction errors. Performance is evaluated in a real context where distinguishing between four different pathologies: control versus bacterial pneumonia versus viral pneumonia versus COVID-19. Our system differentiates between pneumonia patients and controls with an accuracy of 97.74%, whereas in the 4-class context the accuracy is 86.73%. The excellent results and the pioneering use of sparse coding in this scenario evidence that our proposal can assist clinicians when their workload is high.

CGENet: A Deep Graph Model for COVID-19 Detection Based on Chest CT.

Accurate and timely diagnosis of COVID-19 is indispensable to control its spread. This study proposes a novel explainable COVID-19 diagnosis system called CGENet based on graph embedding and an extreme learning machine for chest CT images. We put forward an optimal backbone selection algorithm to select the best backbone for the CGENet based on transfer learning. Then, we introduced graph theory into the ResNet-18 based on the k-nearest neighbors. Finally, an extreme learning machine was trained as the classifier of the CGENet. The proposed CGENet was evaluated on a large publicly-available COVID-19 dataset and produced an average accuracy of 97.78% based on 5-fold cross-validation. In addition, we utilized the Grad-CAM maps to present a visual explanation of the CGENet based on COVID-19 samples. In all, the proposed CGENet can be an effective and efficient tool to assist COVID-19 diagnosis.

NAGNN: Classification of COVID‐19 based on neighboring aware representation from deep graph neural network

COVID‐19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer‐aided diagnosis system based on artificial intelligence to automatically identify the COVID‐19 in chest computed tomography images. We utilized transfer learning to obtain the image‐level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k‐nearest neighbors algorithm, in which the ILRs were linked with their k‐nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end‐to‐end COVID‐19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state‐of‐the‐art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID‐19, which can be used in clinical diagnosis.

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

Objective: COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. Methods: First, the n-conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network (12l-CNN) as the backbone network. Afterwards, PatchShuffle was introduced to integrate with 12l-CNN as a regularization term of the loss function. Our model was named PSCNN. Moreover, multiple-way data augmentation and Grad-CAM are employed to avoid overfitting and locating lung lesions. Results: The mean and standard variation values of the seven measures of our model were 95.28 ± 1.03 (sensitivity), 95.78 ± 0.87 (specificity), 95.76 ± 0.86 (precision), 95.53 ± 0.83 (accuracy), 95.52 ± 0.83 (F1 score), 91.7 ± 1.65 (MCC), and 95.52 ± 0.83 (FMI). Conclusion: Our PSCNN is better than 10 state-of-the-art models. Further, we validate the optimal hyperparameters in our model and demonstrate the effectiveness of PatchShuffle.

MFBCNNC: Momentum factor biogeography convolutional neural network for COVID-19 detection via chest X-ray images.

AIM: By October 6, 2020, Coronavirus disease 2019 (COVID-19) was diagnosed worldwide, reaching 3,355,7427 people and 1,037,862 deaths. Detection of COVID-19 and pneumonia by the chest X-ray images is of great significance to control the development of the epidemic situation. The current COVID-19 and pneumonia detection system may suffer from two shortcomings: the selection of hyperparameters in the models is not appropriate, and the generalization ability of the model is poor. METHOD: To solve the above problems, our team proposed an improved intelligent global optimization algorithm, which is based on the biogeography-based optimization to automatically optimize the hyperparameters value of the models according to different detection objectives. In the optimization progress, after selecting the immigration of suitable index vector and the emigration of suitable index vector, we proposed adding a comparison operation to compare the value of them. According to the different numerical relationships between them, the corresponding operations are performed to improve the migration operation of biogeography-based optimization. The improved algorithm (momentum factor biogeography-based optimization) can better perform the automatic optimization operation. In addition, our team also proposed two frameworks: biogeography convolutional neural network and momentum factor biogeography convolutional neural network. And two methods for detection COVID-19 based on the proposed frameworks. RESULTS: Our method used three convolutional neural networks (LeNet-5, VGG-16, and ResNet-18) as the basic classification models for chest X-ray images detection of COVID-19, Normal, and Pneumonia. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 1.56%, 1.48%, and 0.73% after using biogeography-based optimization to optimize the hyperparameters of the models. The accuracy of LeNet-5, VGG-16, and ResNet-18 is improved by 2.87%, 6.31%, and 1.46% after using the momentum factor biogeography-based optimization to optimize the hyperparameters of the models. CONCLUSION: Under the same experimental conditions, the performance of the momentum factor biogeography-based optimization is superior to the biogeography-based optimization in optimizing the hyperparameters of the convolutional neural networks. Experimental results show that the momentum factor biogeography-based optimization can improve the detection performance of the state-of-the-art approaches in terms of overall accuracy. In future research, we will continue to use and improve other global optimization algorithms to enhance the application ability of deep learning in medical pathological image detection.

CSGBBNet: An Explainable Deep Learning Framework for COVID-19 Detection.

The COVID-19 virus has swept the world and brought great impact to various fields, gaining wide attention from all walks of life since the end of 2019. At present, although the global epidemic situation is leveling off and vaccine doses have been administered in a large amount, confirmed cases are still emerging around the world. To make up for the missed diagnosis caused by the uncertainty of nucleic acid polymerase chain reaction (PCR) test, utilizing lung CT examination as a combined detection method to improve the diagnostic rate becomes a necessity. Our research considered the time-consuming and labor-intensive characteristics of the traditional CT analyzing process, and developed an efficient deep learning framework named CSGBBNet to solve the binary classification task of COVID-19 images based on a COVID-Seg model for image preprocessing and a GBBNet for classification. The five runs with random seed on the test set showed our novel framework can rapidly analyze CT scan images and give out effective results for assisting COVID-19 detection, with the mean accuracy of 98.49 ± 1.23%, the sensitivity of 99.00 ± 2.00%, the specificity of 97.95 ± 2.51%, the precision of 98.10 ± 2.61%, and the F1 score of 98.51 ± 1.22%. Moreover, our model CSGBBNet performs better when compared with seven previous state-of-the-art methods. In this research, the aim is to link together biomedical research and artificial intelligence and provide some insights into the field of COVID-19 detection.

Deep Fractional Max Pooling Neural Network for COVID-19 Recognition.

Aim: Coronavirus disease 2019 (COVID-19) is a form of disease triggered by a new strain of coronavirus. This paper proposes a novel model termed "deep fractional max pooling neural network (DFMPNN)" to diagnose COVID-19 more efficiently. Methods: This 12-layer DFMPNN replaces max pooling (MP) and average pooling (AP) in ordinary neural networks with the help of a novel pooling method called "fractional max-pooling" (FMP). In addition, multiple-way data augmentation (DA) is employed to reduce overfitting. Model averaging (MA) is used to reduce randomness. Results: We ran our algorithm on a four-category dataset that contained COVID-19, community-acquired pneumonia, secondary pulmonary tuberculosis (SPT), and healthy control (HC). The 10 runs on the test set show that the micro-averaged F1 (MAF) score of our DFMPNN is 95.88%. Discussions: This proposed DFMPNN is superior to 10 state-of-the-art models. Besides, FMP outperforms traditional MP, AP, and L2-norm pooling (L2P).

SARS-Net: COVID-19 detection from chest x-rays by combining graph convolutional network and convolutional neural network.

COVID-19 has emerged as one of the deadliest pandemics that has ever crept on humanity. Screening tests are currently the most reliable and accurate steps in detecting severe acute respiratory syndrome coronavirus in a patient, and the most used is RT-PCR testing. Various researchers and early studies implied that visual indicators (abnormalities) in a patient's Chest X-Ray (CXR) or computed tomography (CT) imaging were a valuable characteristic of a COVID-19 patient that can be leveraged to find out virus in a vast population. Motivated by various contributions to open-source community to tackle COVID-19 pandemic, we introduce SARS-Net, a CADx system combining Graph Convolutional Networks and Convolutional Neural Networks for detecting abnormalities in a patient's CXR images for presence of COVID-19 infection in a patient. In this paper, we introduce and evaluate the performance of a custom-made deep learning architecture SARS-Net, to classify and detect the Chest X-ray images for COVID-19 diagnosis. Quantitative analysis shows that the proposed model achieves more accuracy than previously mentioned state-of-the-art methods. It was found that our proposed model achieved an accuracy of 97.60% and a sensitivity of 92.90% on the validation set.