RESCOVIDTCNnet: A residual neural network-based framework for COVID-19 detection using TCN and EWT with chest X-ray images.

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Abstract

Since the emergence of COVID-19, there has been an exponential surge in the number of casualties which increases the demand for numerous research works that can successfully detect the disease accurately in the early stage. This study provides some methods based on deep learning for the diagnosis of patients suffering from COVID disease, healthy controls, and pneumonia classes using chest X-rays. The methodology consists of four main phases: data acquisition, pre-processing, feature extraction, and classification. The chest X-rays images used in this study were obtained from various publicly available databases. In the pre-processing step, the images were filtered to improve the image quality, and empirical wavelet transform (EWT) was used to de-noise the chest X-ray images. Next, feature extraction via four deep learning models was attempted. The first two models are based on transfer learning models: Inception-V3 and Resnet-50. The third model is developed by combining the Resnet-50 with temporal convolutional neural network (TCN). The fourth model is our proposed model known as RESCOVIDTCNNet which combines EWT with Resnet-50 and TCN. Finally, the classification was performed by artificial neural network (ANN) and support vector machine (SVM). Our proposed RESCOVIDTCNNet has yielded an accuracy of 99.5% using five-fold cross-validation for 3-class classification. Our prototype has the potential to be used in underdeveloped countries where there is an acute shortage of radiologists to obtain the diagnosis immediately.© 2022 Published by Elsevier Ltd.