@inproceedings{070cbcb8b17f4405a0b4257355583f96,
title = "COVID-19 Chest X-Ray Classification Using Compact Convolutional Transformer",
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.",
keywords = "CCT, Chest X-Ray, Compact Convolution Transformer, Covid-19, CXR",
author = "Tan, {Xin Hui} and {Yan Lim}, Jit and Lim, {Kian Ming} and Lee, {Chin Poo}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 11th International Conference on Information and Communication Technology, ICoICT 2023 ; Conference date: 23-08-2023 Through 24-08-2023",
year = "2023",
doi = "10.1109/ICoICT58202.2023.10262549",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "266--270",
booktitle = "2023 11th International Conference on Information and Communication Technology, ICoICT 2023",
address = "United States",
}