🤖 AI Summary
To address the low sensitivity and long turnaround time of RT-PCR testing in COVID-19 diagnosis, this study proposes a transfer learning–based automated classification method for chest X-ray (CXR) images. We employ the EfficientNet-B4 architecture, integrated with data augmentation, hierarchical fine-tuning, learning rate decay, and five-fold cross-validation, and systematically evaluate its generalizability across multiple pulmonary diseases—including COVID-19—within the PyTorch framework. Experimental results demonstrate 100% accuracy on a dataset of 2,000 COVID-19 CXR images and achieve 99.17% accuracy and 99.14% F1-score on a larger multi-class dataset comprising 4,350 pulmonary X-ray images—outperforming mainstream architectures such as ResNet and DenseNet. This work provides the first empirical validation of EfficientNet-B4’s exceptional robustness and cross-disease generalization capability in CXR-based diagnosis, establishing a reliable, lightweight, and high-accuracy pathway for AI-assisted screening of respiratory infections.
📝 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.
