Secure pulmonary diagnosis using transformer-based approach to x-ray classification with KL divergence optimization

Introduction:
Lung disease classification plays a significant part in the early discovery and determination of respiratory conditions.

Methods:
This paper proposes a novel approach for lung disease classification utilizing two advanced deep learning models, MedViT and Swin Transformer, applied to the Lung X-Ray Image Dataset that includes 10,425 X-ray images categorized into three classes: Normal with 3,750 images, Lung Opacity with 3,375 images, and Viral Pneumonia with 3,300 images. A series of data augmentation methods, including geometric and photometric augmentation, are applied to improve model performance and generalization.

Results:
The results illustrate that both MedViT and Swin Transformer accomplish promising classification accuracy, with MedViT showing particular strength in medical image-specific feature learning due to its hybrid convolutional and transformer design. The impact of different loss functions is also examined, where Kullback-Leibler Divergence yields the highest accuracy and effectively handles class imbalance. The best-performing MedViT model achieves an accuracy of 98.6% with a loss of 0.09.

Discussion:
These findings highlight the potential of transformer-based models, particularly MedViT, for reliable clinical decision support in automated lung disease classification.