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Open Access

Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network

Department of ECE, KIET Group of Institutions, Delhi-NCR, Ghaziabad 201206, India
Department of CSE, ASET, Amity University Uttar Pradesh, Noida 201310, India
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Abstract

Coronavirus disease 2019 also known as COVID-19 has become a pandemic. The disease is caused by a beta coronavirus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). The severity of the disease can be understood by the massive number of deaths and affected patients globally. If the diagnosis is fast-paced, the disease can be controlled in a better manner. Laboratory tests are available for diagnosis, but they are bounded by available testing kits and time. The use of radiological examinations that comprise Computed Tomography (CT) can be used for the diagnosis of the disease. Specifically, chest X-Ray images can be analysed to identify the presence of COVID-19 in a patient. In this paper, an automated method for the diagnosis of COVID-19 from the chest X-Ray images is proposed. The method presents an improved depthwise convolution neural network for analysing the chest X-Ray images. Wavelet decomposition is applied to integrate multiresolution analysis in the network. The frequency sub-bands obtained from the input images are fed in the network for identifying the disease. The network is designed to predict the class of the input image as normal, viral pneumonia, and COVID-19. The predicted output from the model is combined with Grad-CAM visualization for diagnosis. A comparative study with the existing methods is also performed. The metrics like accuracy, sensitivity, and F1-measure are calculated for performance evaluation. The performance of the proposed method is better than the existing methodologies and thus can be used for the effective diagnosis of the disease.

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Big Data Mining and Analytics
Pages 84-93
Cite this article:
Singh KK, Singh A. Diagnosis of COVID-19 from Chest X-Ray Images Using Wavelets-Based Depthwise Convolution Network. Big Data Mining and Analytics, 2021, 4(2): 84-93. https://doi.org/10.26599/BDMA.2020.9020012

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Received: 05 June 2020
Revised: 19 July 2020
Accepted: 28 July 2020
Published: 01 February 2021
© The author(s) 2021

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

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