Citation

Younis, Muhammad Hamza and Abbas, Safdar and Hayat, Umar and Musaddiq, Muhammad Hammad and Hashmi, Adeel (2024) A Cutting-Edge Hybrid Approach for Precise COVID-19 Detection using Deep Learning. International Journal on Robotics, Automation and Sciences, 6 (1). pp. 86-93. ISSN 2682-860X

Text 556 - Published Version Restricted to Repository staff only Download (2kB)

Abstract

The early detection of COVID-19 is essential for decision-makers to develop effective containment and treatment plans. Traditionally, researchers interpret computer tomography (CT) scans or X-ray images in order to diagnose this disease. This study aims to demonstrate that deep learning models can be applied to three common medical imaging modes: X-rays, ultrasounds, and CT scans. This study employs and enhances four convolutional neural networks for coronavirus detection, including DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2. In this study, two main experiments were carried out. In the first experiment, a model was developed by combining imagery data to detect this virus. In order to determine which model performed the best, separate models were trained using different datasets in the second experiment. Because there were only so many photos accessible, data augmentation techniques were used to enhance the amount artificially. The results indicate that the proposed models effectively accomplished the task of classifying COVID-19. The accuracy rates achieved by the combined model, utilizing DenseNet121, ResNet101V2, NASNetMobile, and MobileNetV2, were 88.21%, 93.02%, and 88.89% respectively. When using the combined imaging dataset, the CNN model employing ResNet101v2 exhibited superior accuracy compared to NASNetMobile, DenseNet121, and MobileNetV2 models.

Item Type:	Article
Uncontrolled Keywords:	X-rays, CT-Scan, Covid19, Deep Learning, NASNetMobile
Subjects:	R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions:	Others
Depositing User:	Mr. MUHAMMAD AZRUL MOSRI
Date Deposited:	06 Sep 2024 07:24
Last Modified:	06 Sep 2024 07:24
URII:	http://shdl.mmu.edu.my/id/eprint/12972

Downloads

Edit (login required)