CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Khan, Asif Iqbal; Shah, Junaid Latief; Bhat, Mohammad Mudasir

doi:10.1016/j.cmpb.2020.105581

Cited by 1,111 publications

(1,051 citation statements)

References 14 publications

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“… Approach Number of original samples Number of classes Accuracy Average precision Average Recall Average F score MobileNet [14] 224 COVID-19 504 Healthy 714 Pneumonia (400 bacterial ＋ 314 viral) 2 classes 96.78% 96.46% 98.66% – DarkCovidNet [15] 125 COVID-19 500 No- Findings 2 classes 98.08%. 98.03% 95.13% 96.51% DarkCovidNet [15] 125 COVID-19 500 No- Findings 500 Pneumonia 3 classes 87.02% 89.96% 85.35% 87.37% CNN-SA [16] 403 COVID-19 721 Normal 2 classes 95% 95% 95% 95% CoroNet [17] 284 COVID-19 310 Normal 330 Pneumonia Bacterial 327 Pneumonia Viral 4 classes 89.6% 90% 89.92% 89.8% CoroNet [17] 284 COVID-19 310 Normal 657 Pneumonia (330 bacterial ＋ 327 viral) 3 classes …”

Section: Experiments Resultsmentioning

confidence: 99%

“…The proposed GSA-DenseNet121-COVID-19 performance was also compared with other published approaches that were introduced for the same purpose of diagnosing COVID-19 using X-ray images. The other approaches included in [14] , [15] , [16] , [17] , [19] were selected for comparison, as they relied on CNN architectures and were trained on a variety of data samples. The proposed approach has been compared to other approaches in terms of the number and variety of samples used, accuracy, precision, recall, and f score, as shown in Table 8 .…”

Section: Experiments Resultsmentioning

confidence: 99%

“…The experiment results demonstrated that the CovidGAN model helped the VGG16 network diagnose the COVID-19 with 95% accuracy. In [17] , the authors produced a model called CoroNet, and this model was able to diagnose COVID-19 with 89.6% accuracy in a four-category classification (COVID-19 vs. bacterial pneumonia vs. viral pneumonia vs. normal). In a three-category classification (COVID vs. pneumonia vs. normal), the CoroNet model achieved 94% accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat

Hassanien

Ella

2021

Applied Soft Computing

159

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In this paper, a novel approach called GSA-DenseNet121-COVID-19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121, and the optimization algorithm that was used is called the gravitational search algorithm (GSA). The GSA is used to determine the best values for the hyperparameters of the DenseNet121 architecture. To help this architecture to achieve a high level of accuracy in diagnosing COVID-19 through chest x-ray images. The obtained results showed that the proposed approach could classify 98.38% of the test set correctly. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121. The GSA was compared to another approach called SSD-DenseNet121, which depends on the DenseNet121 and the optimization algorithm called social ski driver (SSD). The comparison results demonstrated the efficacy of the proposed GSA-DenseNet121-COVID-19. As it was able to diagnose COVID-19 better than SSD-DenseNet121 as the second was able to diagnose only 94% of the test set. The proposed approach was also compared to another method based on a CNN architecture called Inception-v3 and manual search to quantify hyperparameter values. The comparison results showed that the GSA-DenseNet121-COVID-19 was able to beat the comparison method, as the second was able to classify only 95% of the test set samples. The proposed GSA-DenseNet121-COVID-19 was also compared with some related work. The comparison results showed that GSA-DenseNet121-COVID-19 is very competitive.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: Experiments Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Ezzat

Hassanien

Ella

2021

Applied Soft Computing

159

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“…Also, they tested all five deep learning models for a binary classification problem (i.e., COVID-19 against None-COVID-19) and they achieved the highest accuracy of 98.75% using VGG-19. Khan et al [21] proposed a deep learning convolutional neural network (i.e., CoroNet) to diagnose COVID-19 in multi-class problem from the whole chest X-ray images. They achieved overall accuracy of 89.6% for COVID-19 against pneumonia bacterial, pneumonia viral, and normal cases.…”

Section: Related Workmentioning

confidence: 99%

Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

Al-antari

Hua

Lee

2020

Preprint

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Background and Objective: The novel coronavirus 2019 (COVID-19) is a harmful lung disease that rapidly attacks people worldwide. At the end of 2019, COVID-19 was discovered as mysterious lung disease in Wuhan, Hubei province of China. World health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 from the entire digital X-ray images is the key to efficiently assist patients and physicians for a fast and accurate diagnosis.Methods: In this paper, a deep learning computer-aided diagnosis (CAD) based on the YOLO predictor is proposed to simultaneously detect and diagnose COVID-19 among the other eight lung diseases: Atelectasis, Infiltration, Pneumothorax, Mass, Effusion, Pneumonia, Cardiomegaly, and Nodule. The proposed CAD system is assessed via five-fold tests for multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system is trained using an annotated training set of 50,490 chest X-ray images.Results: The suspicious regions of COVID-19 from the entire X-ray images are simultaneously detected and classified end-to-end via the proposed CAD predictor achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. The most testing images of COVID-19 and other lunge diseases are correctly predicted achieving intersection over union (IoU) with their GTs greater than 90%. Applying deep learning regularizers of data balancing and augmentation improve the diagnostic performance by 6.64% and 12.17% in terms of overall accuracy and F1-score, respectively. Meanwhile, the proposed CAD system presents its feasibility to diagnose the individual chest X-ray image within 0.009 second. Thus, the presented CAD system could predict 108 frames/second (FPS) at the real-time of prediction.Conclusion: The proposed deep learning CAD system shows its capability and reliability to achieve promising COVID-19 diagnostic performance among all other lung diseases. The proposed deep learning model seems reliable to assist health care systems, patients, and physicians in their practical validations.

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“…Asif et al proposed CoroNet model based on Xception architecture using X-ray images to differentiate COVID-19 from heathy, bacterial pneumonia and viral pneumonia 12 . Notably, Xception is a transfer learning model which was pertained on ImageNet dataset and then retained on the collected X-ray dataset.…”

Section: Introductionmentioning

confidence: 99%

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

Liu

Wang

Liu

et al. 2020

Preprint

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Background: Chest CT screening as supplementary means is crucial in diagnosing novel coronavirus pneumonia (COVID-19) with high sensitivity and popularity. Machine learning was adept in discovering intricate structures from CT images and achieved expert-level performance in medical image analysis. Methods: An integrated machine learning framework on chest CT images for differentiating COVID-19 from general pneumonia (GP) was developed and validated. Seventy-three confirmed COVID-19 cases were consecutively enrolled together with twenty-seven confirmed general pneumonia patients from Ruian People’s Hospital, from January 2020 to March 2020. To accurately classify COVID-19, region of interest (ROI) delineation was implemented based on ground glass opacities (GGOs) before feature extraction. Then, 34 statistical texture features of COVID-19 and GP ROI images were extracted, including 13 gray level co-occurrence matrix (GLCM) features, 15 gray level-gradient co-occurrence matrix (GLGCM) features and 6 histogram features. High dimensional features impact the classification performance. Thus, ReliefF algorithm was leveraged to select features. The relevance of each features was the average weights calculated by ReliefF in n times. Features with relevance lager than the empirically set threshold T were selected. After feature selection, the optimal feature set along with 4 other selected feature combinations for comparison were applied to the ensemble of bagged tree (EBT) and four other machine learning classifiers including support vector machine (SVM), logistic regression (LR), decision tree (DT), and K-nearest neighbor with Minkowski distance equal weight (KNN) using 10-fold cross-validation. Results and Conclusions: The classification accuracy (ACC), sensitivity (SEN), specificity (SPE) of our proposed method yield 94.16%, 88.62% and 100.00%, respectively. The area under the receiver operating characteristic curve (AUC) was 0.99. The experimental results indicate that the EBT algorithm with statistical textural features based on GGOs for differentiating COVID-19 from general pneumonia achieved high transferability, efficiency, specificity, sensitivity, and impressive accuracy, which is beneficial for inexperienced doctors to more accurately diagnose COVID-19 and essential for controlling the spread of the disease.

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CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images

Cited by 1,111 publications

References 14 publications

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

An optimized deep learning architecture for the diagnosis of COVID-19 disease based on gravitational search optimization

Fast Deep Learning Computer-Aided Diagnosis against the Novel COVID-19 pandemic from Digital Chest X-ray Images

Differentiating novel coronavirus pneumonia from general pneumonia based on machine learning

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