Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Abbas, Asmaa; Abdelsamea, Mohammed M.; Gaber, Mohamed Medhat

doi:10.1007/s10489-020-01829-7

Cited by 728 publications

(260 citation statements)

References 35 publications

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“…Seventeen studies used publicly available datasets and 10 studies disclosed source codes. Three studies used independent datasets, one was disclosed (9), one was not disclosed (33), and one of them was pending (35) until July 21, 2020. One of issues that need to be considered when releasing datasets is ethical considerations, and most of the studies done by the datasets have been cleared of these problems.…”

Section: Discussionmentioning

confidence: 99%

“…All papers used deep learning as AI technique. Most papers have used novel (9,(16)(17)(18)21,22,27,30,35) or modified (20,23,24,26,28,(32)(33)(34) AI software based on deep learning-based neural networks. The COVID-19 prediction is the main topic of most papers and issues such as the severity of COVID-19 (35), uncertainty for COVID-19 detection (23), and classification of pulmonary diseases (20) have also been considered.…”

Section: Discussionmentioning

confidence: 99%

“…Abbas et al (33) adapted their previous CNN model, called DeTraC model, to improve the performance of pretrained models in COVID-19 detection from chest x-rays. They disclosed the source code.…”

Section: Studies Published On Arxivmentioning

confidence: 99%

See 2 more Smart Citations

A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

Sadeghi

Omidi

Sina

2020

Avicenna J Med Biochem

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Background: In this study, the artificial intelligence (AI) techniques used for the detection of coronavirus disease 2019 (COVID-19) from the chest x-ray were reviewed. Methods: PubMed, arXiv, and Google Scholar were used to search for AI studies. Results: A total of 20 papers were extracted from Google Scholar, 14 from arXiv, and 5 from PubMed. In 17 papers, publicly available datasets and in 3 papers, independent datasets were used. 10 papers disclosed source codes. Nine papers were about creating a novel AI software, 8 papers reported the modification of the existing AI models, and 3 compared the performance of the existing AI software programs. All papers have used deep learning as AI technique. Most papers reported accuracy, specificity, and sensitivity of the models, and also the area under the curve (AUC) for investigation of the model performance for the prediction of COVID-19. Nine papers reported accuracy, sensitivity, and specificity. The number of datasets used in the studies ranged from 50 to 94323. The accuracy, sensitivity, and specificity of the models ranged from 0.88 to 0.98, 0.80 to 1.00, and 0.70 to 1.00, respectively. Conclusion: The studies revealed that AI can help human in fighting the new Coronavirus.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

Sadeghi

Omidi

Sina

2020

Avicenna J Med Biochem

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“…Through progressive resizing of input images and network fine-tuning, the study in [22] involves the use of pre-trained ResNet-50 architecture for the screening of COVID-19 from chest X-rays called COVID-ResNet. The study from [1] introduces a deep CNN method called DeTraC for the identification of COVID-19 from chest radiographs. DeTraC stands for Decompose, Transfer, and Compose and involves a class decomposition method to handle irregularities in data.…”

Section: Related Workmentioning

confidence: 99%

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Singh

Pandey

Babu

2021

Neural Comput & Applic

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COVID-19 has emerged as a global crisis with unprecedented socioeconomic challenges, jeopardizing our lives and livelihoods for years to come. The unavailability of vaccines for COVID-19 has rendered rapid testing of the population instrumental in order to contain the exponential rise in cases of infection. Shortage of RT-PCR test kits and delays in obtaining test results calls for alternative methods of rapid and reliable diagnosis. In this article, we propose a novel deep learning-based solution using chest X-rays which can help in rapid triaging of COVID-19 patients. The proposed solution uses image enhancement, image segmentation, and employs a modified stacked ensemble model consisting of four CNN base-learners along with Naive Bayes as meta-learner to classify chest X-rays into three classes viz. COVID-19, pneumonia, and normal. An effective pruning strategy as introduced in the proposed framework results in increased model performance, generalizability, and decreased model complexity. We incorporate explainability in our article by using Grad-CAM visualization in order to establish trust in the medical AI system. Furthermore, we evaluate multiple state-ofthe-art GAN architectures and their ability to generate realistic synthetic samples of COVID-19 chest X-rays to deal with limited numbers of training samples. The proposed solution significantly outperforms existing methods, with 98.67% accuracy, 0.98 Kappa score, and F-1 scores of 100, 98, and 98 for COVID-19, normal, and pneumonia classes, respectively, on standard datasets. The proposed solution can be used as one element of patient evaluation along with goldstandard clinical and laboratory testing.

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“…The image dataset is a publicly accessible COVID-19 CXR dataset [8], which is derived from the COVID-19 Radiography Database. 1 It contains 219 COVID-19, 1345 viral pneumonia, and 1341 normal CXR images. Table 4 shows the details of the dataset.…”

Section: Dataset Detailsmentioning

confidence: 99%

Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification

Annavarapu

2021

Appl Intell

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COVID-19 has proven to be a deadly virus, and unfortunately, it triggered a worldwide pandemic. Its detection for further treatment poses a severe threat to researchers, scientists, health professionals, and administrators worldwide. One of the daunting tasks during the pandemic for doctors in radiology is the use of chest X-ray or CT images for COVID-19 diagnosis. Time is required to inspect each report manually. While a CT scan is the better standard, an X-ray is still useful because it is cheaper, faster, and more widely used. To diagnose COVID-19, this paper proposes to use a deep learning-based improved Snapshot Ensemble technique for efficient COVID-19 chest X-ray classification. In addition, the proposed method takes advantage of the transfer learning technique using the ResNet-50 model, which is a pre-trained model. The proposed model uses the publicly accessible COVID-19 chest X-ray dataset consisting of 2905 images, which include COVID-19, viral pneumonia, and normal chest X-ray images. For performance evaluation, the model applied the metrics such as AU-ROC, AU-PR, and Jaccard Index. Furthermore, it also obtained a multi-class micro-average of 97% specificity, 95% f 1-score, and 95% classification accuracy. The obtained results demonstrate that the performance of the proposed method outperformed those of several existing methods. This method appears to be a suitable and efficient approach for COVID-19 chest X-ray classification.

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Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network

Cited by 728 publications

References 35 publications

A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

A Systematic Review on the Use of Artificial Intelligence Techniques in the Diagnosis of COVID-19 from Chest X-Ray Images

COVIDScreen: explainable deep learning framework for differential diagnosis of COVID-19 using chest X-rays

Deep learning-based improved snapshot ensemble technique for COVID-19 chest X-ray classification

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