Empowering COVID-19 Detection: Optimizing Performance Through Fine-Tuned
  EfficientNet Deep Learning Architecture
Md. Alamin Talukder Md. Abu Layek Mohsin Kazi Md Ashraf Uddin Sunil Aryal
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 100% for EfficientNetB4
model. 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.