COVID-19 Chest X-Ray Classification Using Residual Network

Xin Hui Tan, Jit Yan Lim, Kian Ming Lim, Chin Poo Lee

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

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 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.

Original languageEnglish
Title of host publication11th International Conference on Information and Communication Technology, ICoICT 2023
EditorsLee-Ying Chong, Tee Connie, Dawam Dwi Jatmiko Suwawi, Joon Liang Tan
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages271-276
Number of pages6
ISBN (Electronic)9798350321982
ISBN (Print)9798350333039
DOIs
Publication statusPublished - 2023
Externally publishedYes
EventInternational Conference on Information and Communication Technology 2023 - Melaka, Malaysia
Duration: 23 Aug 202324 Aug 2023
Conference number: 11th
https://ieeexplore.ieee.org/xpl/conhome/10262402/proceeding (Proceedings)
https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&ved=2ahUKEwiKrtbNp-GEAxUbs1YBHZlDAVkQFnoECA8QAQ&url=https%3A%2F%2Fwww.icoict.org%2F2023-icoict%2F&usg=AOvVaw2TRUvwZYzGEHUrk65UNNeI&opi=89978449 (Website)

Conference

ConferenceInternational Conference on Information and Communication Technology 2023
Abbreviated titleICoICT 2023
Country/TerritoryMalaysia
CityMelaka
Period23/08/2324/08/23
Internet address

Keywords

  • Chest Xray
  • Covid-19
  • ResNet152
  • transfer learning

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