Abstract: Data Analytics of COVID-19 is not just enough for curbing down this deadly disease which now has become a pandemic. The detection of Severe Acute Respiratory Syndrome corona virus 2 (SARS cov-2), which is responsible for corona virus disease 2019 (COVID-19), using chest X-ray images has life- saving importance for both patients and doctors. [1] X-rays are cost-effective and widely available at public health facilities and hospital emergency rooms, they could be used for rapid detection of possible COVID-19-induced lung infections. Therefore, towards automating the COVID-19 detection, in this paper, we propose a viable and efficient Deep Learning Based Chest Radiograph Classification (DL-CRC) framework to distinguish the COVID-19 cases with high accuracy from other abnormal and normal cases. A unique dataset is prepared from four publicly available sources containing the chest view of X-ray data for COVID-19, pneumonia, and normal cases. Our system consists of Convolution Neural Network (CNN) architecture capable of detecting masked and unmasked faces also which will be dealt with in the next paper. The encouragingly high classification accuracy of our proposal implies that it can efficiently automate COVID-19 detection from radiograph images to provide a fast and reliable evidence of COVID-19 infection in the lung that can complement existing COVID-19 diagnostics modalities.

Keywords: Convolution Neural Network (CNN) architecture, COVID-19, Severe Acute Respiratory Syndrome corona virus 2 (SARS cov-2), deep learning based chest radiograph classification (DL-CRC) and radiography images.

PDF | DOI: 10.17148/IJARCCE.2021.10811

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