Empowering COVID-19 detection: Optimizing performance through fine-tuned EfficientNet deep learning architecture.

Journal: Computers in biology and medicine
PMID:

Abstract

The worldwide COVID-19 pandemic has profoundly influenced the health and everyday experiences of individuals across the planet. It is a highly contagious respiratory disease requiring early and accurate detection to curb its rapid transmission. Initial testing methods primarily revolved around identifying the genetic composition of the coronavirus, exhibiting a relatively low detection rate and requiring a time-intensive procedure. To address this challenge, experts have suggested using radiological imagery, particularly chest X-rays, as a valuable approach within the diagnostic protocol. This study investigates the potential of leveraging radiographic imaging (X-rays) with deep learning algorithms to swiftly and precisely identify COVID-19 patients. The proposed approach elevates the detection accuracy by fine-tuning with appropriate layers on various established transfer learning models. The experimentation was conducted on a COVID-19 X-ray dataset containing 2000 images. The accuracy rates achieved were impressive of 99.55%, 97.32%, 99.11%, 99.55%, 99.11% and 100% for Xception, InceptionResNetV2, ResNet50 , ResNet50V2, EfficientNetB0 and EfficientNetB4 respectively. The fine-tuned EfficientNetB4 achieved an excellent accuracy score, showcasing its potential as a robust COVID-19 detection model. Furthermore, EfficientNetB4 excelled in identifying Lung disease using Chest X-ray dataset containing 4,350 Images, achieving remarkable performance with an accuracy of 99.17%, precision of 99.13%, recall of 99.16%, and f1-score of 99.14%. These results highlight the promise of fine-tuned transfer learning for efficient lung detection through medical imaging, especially with X-ray images. This research offers radiologists an effective means of aiding rapid and precise COVID-19 diagnosis and contributes valuable assistance for healthcare professionals in accurately identifying affected patients.

Authors

  • Md Alamin Talukder
    Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh. Electronic address: alamintalukder.cse.jnu@gmail.com.
  • Md Abu Layek
    Department of Computer Science and Engineering, Jagannath University, Dhaka, Bangladesh. Electronic address: layek@cse.jnu.ac.bd.
  • Mohsin Kazi
    Department of Pharmaceutics, College of Pharmacy, King Saud University, P.O. Box-2457, Riyadh 11451, Saudi Arabia. Electronic address: mkazi@ksu.edu.sa.
  • Md Ashraf Uddin
    School of Information Technology, Deakin University, Geelong 3125, Australia.
  • Sunil Aryal
    School of Information Technology, Deakin University, Geelong 3125, Australia.