A Hybrid Deep Learning Approach for Lung Diseases Classification Using Vision Transformer and DenseNet

Lung disorders, such as pneumonia, tuberculosis, and COVID-19, remain significant global health issues. Accurate and early detection is of paramount significance to ensure effective treatment and control. Conventional CNN models like DenseNet have shown superior performance in the classification of medical images, whereas Vision Transformers (ViTs) have recently gained momentum as formidable models to capture global context information. Each of these models, however, has limitations when operated standalone. This manuscript suggests an innovative hybrid deep learning framework that synergizes the benefits of both ViT and DenseNet to advance lung disease diagnosis using chest X-ray images. The hybrid model uses both ViT to learn long-range context and DenseNet to reuse features and transmit gradients. Our study examines our method using the ChestX-ray14 dataset and uses preprocessing to refine images and boost data. The hybrid model performs excellently in comparison to base case architectures of ViT and DenseNet and shows improvement in accuracy, sensitivity, precision, and F1-score. The outcomes demonstrate the merit of the integrated formulation in refining and improving lung disease diagnosis, thereby potentially contributing to precise and punctual diagnosis.