Saad Akbar and Humera Tariq and Muhammad Fahad and Ghufran Ahmed and Hassan Jamil Syed (2022) Contemporary study on deep neural networks to diagnose Covid-19 using digital posteroanterior x-ray images. Electronics, 11. pp. 1-16.
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Abstract
COVID-19 is a transferable disease inherited from the SARS-CoV-2 virus. A total of 594 million people have been infected, and 6.4 million human beings have died due to COVID-19. The fastest way to diagnose the disease is by radiography. Deep learning has been the most popular technique for image classification during the last decade. This paper aims to examine the contributions of machine learning for the detection of COVID-19 using Deep Learning and explores the overall application of convolutional neural networks of some famous state-of-the-art deep learning pretrained models. In this research, our objective is to explore the various image classification strategies for CXIs and the application of deep learning models for optimization and feature selection. The study presented in this article shows that the accuracy of deep learning models when detecting COVID-19 on the basis of chest X-ray images ranges from 93 percent to above 99 percent.
| Item Type: | Article |
|---|---|
| Subjects: | Q Science > QA Mathematics > QA1-939 Mathematics > QA71-90 Instruments and machines R Medicine > RC Internal medicine > RC31-1245 Internal medicine > RC581-951 Specialties of internal medicine > RC705-779 Diseases of the respiratory system |
| Department: | FACULTY > Faculty of Computing and Informatics |
| Depositing User: | SITI AZIZAH BINTI IDRIS - |
| Date Deposited: | 16 Dec 2024 12:02 |
| Last Modified: | 16 Dec 2024 12:02 |
| URI: | https://eprints.ums.edu.my/id/eprint/42305 |
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