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

• Published in: Computers in biology and medicine 2024-01, Vol.168, p.107789-107789, Article 107789
• Main Authors: Talukder, Md Alamin, Layek, Md Abu, Kazi, Mohsin, Uddin, Md Ashraf, Aryal, Sunil
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Chest; Coronaviruses; COVID-19; COVID-19 - diagnostic imaging; COVID-19 Testing; Datasets; Deep Learning; Disease transmission; Humans; Lung diseases; Machine learning; Medical diagnosis; Medical imaging; Pandemics; Patients; Power, Psychological; Respiratory diseases; Transfer learning; Viral diseases; X ray imagery; X-rays
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2023.107789

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.