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 (CNN s) 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.
Highlights
Diagnosis of COVID-19 is typically associated with both the symptoms of pneumonia and Chest X-ray tests [25]
Diagnosis of COVID-19 is typically associated with the symptoms of pneumonia, which can be revealed by genetic and imaging tests
Chest X-ray (CXR) and Computed Tomography (CT) are the imaging techniques that play an important role in the diagnosis of COVID-19 disease
Summary
Diagnosis of COVID-19 is typically associated with both the symptoms of pneumonia and Chest X-ray tests [25]. CNN architecture is one of the most popular deep learning approaches with superior achievements in the medical imaging domain [14]. The primary success of CNN is due to its ability to learn features automatically from domainspecific images, unlike the classical machine learning methods. The popular strategy for training CNN architecture is to transfer learned knowledge from a pre-trained network that fulfilled one task into a new task [19]. This method is faster and easy to apply without the need for a huge annotated dataset for training; many researchers tend to apply this strategy especially with medical imaging
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