Citation

Tan, Xin Hui and Lim, Jit Yan and Lim, Kian Ming and Lee, Chin Poo (2023) COVID-19 Chest X-Ray Classification Using Residual Network. In: 2023 11th International Conference on Information and Communication Technology (ICoICT). IEEE, pp. 271-276. ISBN 979-8-3503-2198-2

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Abstract

In 2019, the Covid-19 pandemic has spread across the globe and causing significant disruptions to daily life. Those who have tested positive for Covid-19 may experience long-term respiratory problems as the virus can damage the lungs. Specifically, patients who have recovered from Covid-19 may develop white spots on their lungs. This can be difficult to distinguish from normal lung tissue. Consequently, researchers have conducted extensive studies on image classification of Covid-19 chest x-rays, which has become a popular topic of investigation over the past two years. In this research, four datasets were utilized for image classification including COVID19 Radiography, Chest X-ray, COVID-19, and CoronaHack datasets. All these datasets were sourced from Kaggle. The pretrained ResNet152 model was used in conjunction with a transfer learning technique. Results indicated that the pretrained ResNet152 with early stopping provided the highest accuracy among the techniques tested. In this research, the COVID-19 Radiography dataset achieved an accuracy of 95.61%, while the Chest X-ray dataset achieved an accuracy of 97.59%. CoronaHack dataset and COVID-19 X-ray dataset achieved accuracies of 93.59% and 100%, respectively.

Item Type:	Book Section
Uncontrolled Keywords:	Covid-19, ResNet152, transfer learning, Chest Xray
Subjects:	R Medicine > RC Internal medicine > RC71-78.7 Examination. Diagnosis
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	31 Oct 2023 06:28
Last Modified:	31 Oct 2023 06:28
URII:	http://shdl.mmu.edu.my/id/eprint/11776

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