Citation

Tan, Xin Hui and Lim, Jit Yan and Lim, Kian Ming and Lee, Chin Poo (2023) COVID-19 Chest X-Ray Classification Using Compact Convolutional Transformer. In: 2023 11th International Conference on Information and Communication Technology (ICoICT), 23-24 August 2023, Melaka, Malaysia.

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Abstract

The outbreak of Covid-19 in 2019 had a significant impact worldwide, causing long-term breathing problems in many affected individuals. Some people may experience white spots on their lungs after recovering from Covid-19, which can be difficult to identify. One promising approach for identifying abnormal lungs is through image classification. In this work, we utilize three datasets for image classification: the COVID-19 Radiography Dataset, the Chest X-ray Dataset, and the COVID-19 Dataset. To achieve accurate classification, a pre-trained Compact Convolution Transformer (CCT) has been utilized with transfer learning. Our results show that the COVID-19 Radiography Dataset achieved an accuracy of 89.28%, the Chest X-ray Dataset achieved 95.11% accuracy, and the COVID-19 X-ray Dataset achieved an impressive 97.50% accuracy. These findings demonstrate the potential of using image classification to identify abnormal lungs and pave the way for further research in this area.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Covid-19, Compact Convolution Transformer, CCT, Chest X-Ray, CXR
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 06:22
Last Modified:	31 Oct 2023 06:22
URII:	http://shdl.mmu.edu.my/id/eprint/11775

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