Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
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Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network Asmaa Abbas1 · Mohammed M. Abdelsamea1,2
· Mohamed Medhat Gaber2
© The Author(s) 2020
Abstract Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNNs) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and a deep CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 93.1% (with a sensitivity of 100%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases. Keywords DeTraC · Covolutional neural networks · COVID-19 detection · Chest X-ray images · Data irregularities
1 Introduction Diagnosis of COVID-19 is typically associated with both the symptoms of pneumonia and Chest X-ray tests [25]. Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Figure 1 shows a negative example of a normal chest X-ray,
This article belongs to the Topical Collection: Artificial Intelligence Applications for COVID-19, Detection, Control, Prediction, and Diagnosis Mohammed M. Abdelsamea
[email protected] Asmaa Abbas [email protected] Mohamed Medhat Gaber [email protected] 1
Mathematics Department, Faculty of Science, Assiut University, Assiut, Egypt
2
School of Computing and Digital Technology, Birmingham City University, Birmingham, UK
a positive one with COVID-19, and a positive one with the severe acute respiratory syndrome (SARS). Several classical machine learning approaches have been previously used for automatic classification of digitised chest images [7, 13]. For example, in [17], three statistical features were calculated from lung texture to discriminate between malignant and benign lung nodules using a Support Vector Machine SVM classifier. A grey-level co-occurrence matrix method was used with Backpropagation Network [22] to classify images from being normal or cancerous. With the availability of enough annotated images, deep learning approaches [1, 3, 30] have demonstrated their superiority over the classical machine learning approaches. CNN a
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