TY - GEN
T1 - COVID-19 Chest X-Ray Classification Using Residual Network
AU - Tan, Xin Hui
AU - Lim, Jit Yan
AU - Lim, Kian Ming
AU - Lee, Chin Poo
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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 COVID-19 Radiography, Chest X-ray, COVID-19, and CoronaHack datasets. All these datasets were sourced from Kaggle. The pre-trained ResNet152 model was used in conjunction with a transfer learning technique. Results indicated that the pre-trained 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.
AB - 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 COVID-19 Radiography, Chest X-ray, COVID-19, and CoronaHack datasets. All these datasets were sourced from Kaggle. The pre-trained ResNet152 model was used in conjunction with a transfer learning technique. Results indicated that the pre-trained 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.
KW - Chest Xray
KW - Covid-19
KW - ResNet152
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85174421924&partnerID=8YFLogxK
U2 - 10.1109/ICoICT58202.2023.10262734
DO - 10.1109/ICoICT58202.2023.10262734
M3 - Conference Proceeding
AN - SCOPUS:85174421924
T3 - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
SP - 271
EP - 276
BT - 2023 11th International Conference on Information and Communication Technology, ICoICT 2023
T2 - 11th International Conference on Information and Communication Technology, ICoICT 2023
Y2 - 23 August 2023 through 24 August 2023
ER -