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Detection of COVID-19 from Chest X-ray and CT Scan Images using Improved Stacked Sparse Autoencoder

Syahril Ramadhan Saufi, Muhd Danial Abu Hasan, Zair Asrar Ahmad, Mohd Salman Leong and Lim Meng Hee

Pertanika Journal of Social Science and Humanities, Volume 29, Issue 3, July 2021

DOI: https://doi.org/10.47836/pjst.29.3.14

Keywords: COVID-19, CT scan, deep learning, image classification, X-ray

Published on: 31 July 2021

The novel Coronavirus 2019 (COVID-19) has spread rapidly and has become a pandemic around the world. So far, about 44 million cases have been registered, causing more than one million deaths worldwide. COVID-19 has had a devastating impact on every nation, particularly the economic sector. To identify the infected human being and prevent the virus from spreading further, easy, and precise screening is required. COVID-19 can be potentially detected by using Chest X-ray and computed tomography (CT) images, as these images contain essential information of lung infection. This radiology image is usually examined by the expert to detect the presence of COVID-19 symptom. In this study, the improved stacked sparse autoencoder is used to examine the radiology images. According to the result, the proposed deep learning model was able to achieve a classification accuracy of 96.6% and 83.0% for chest X-ray and chest CT-scan images, respectively.

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ISSN 0128-7702

e-ISSN 2231-8534

Article ID

JST-2352-2020

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