Bridging efficiency and interpretability: Explainable AI for multi-classification of pulmonary diseases utilizing modified lightweight CNNs

Abstract

Pulmonary diseases are notable global health challenges that contribute to increased morbidity and mortality rates. Early and accurate diagnosis is essential for effective treatment. However, traditional apprehension of chest X-ray images is tiresome and susceptible to human error, particularly in resource-constrained settings. Current progress in deep learning, particularly convolutional neural networks, has enabled the automated classification of pulmonary diseases with increased accuracy. In this study, we have proposed an explainable AI approach using modified lightweight convolution neural networks, such as MobileNetV2, EfficientNet-B0, NASNetMobile, and ResNet50V2 to achieve efficient and interpretable classification of multiple pulmonary diseases. Lightweight CNNs are designed to minimize computational complexity while maintaining robust performance, making them ideal for mobile and embedded systems with limited processing power deployment. Our models demonstrated strong performance in detecting pulmonary diseases, with EfficientNet-B0 achieving an accuracy of 94.07%, precision of 94.16%, recall of 94.07%, and F1 score of 94.04%. Furthermore, we have incorporated explainability methods (grad-CAM & t-SNE) to enhance the transparency of model predictions, providing clinicians with a trustworthy tool for diagnostic decision support. The results suggest that lightweight CNNs effectively balance accuracy, efficiency, and interpretability, making them suitable for real-time pulmonary disease detection in clinical and low-resource environments