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- Title
Detection of COVID-19 from Chest X-ray and CT Scan Images using Improved Stacked Sparse Autoencoder.
- Authors
Saufi, Syahril Ramadhan; Abu Hasan, Muhd Danial; Ahmad, Zair Asrar; Leong, Mohd Salman; Hee, Lim Meng
- Abstract
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.
- Subjects
COVID-19; COMPUTED tomography; SARS-CoV-2; DEEP learning; VIRAL transmission; LUNG infections; MULTIDETECTOR computed tomography
- Publication
Pertanika Journal of Science & Technology, 2021, Vol 29, Issue 3, p2045
- ISSN
0128-7680
- Publication type
Article
- DOI
10.47836/pjst.29.3.14