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, Lim Meng Hee
<span title="2021-07-19">2021</span> <i title="Universiti Putra Malaysia"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/dfgafyjk6ndobmolt2xhubrzyi" style="color: black;">Pertanika journal of science &amp; technology</a> </i> &nbsp;
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
more &raquo; ... ) 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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