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

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 deepCNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images.DeTraCcan deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability ofDeTraCin 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 byDeTraCin the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

Automated summary

This paper validates a deep CNN model called DeTraC, which utilizes a class decomposition mechanism to handle irregularities in medical image datasets. Experimental results demonstrate the high accuracy of DeTraC in detecting COVID-19 X-ray images.