Explainable AI Techniques with VGG-16 for Detecting COVID-19

Timely and accurate detection and classification of lung diseases are critical for effective diagnosis and treatment planning. This paper presents a deep learning approach to distinguish COVID-19 from normal cases using chest X-ray images, employing the VGG-16 convolutional neural network (CNN) model. To enhance interpretability and trust in the model's decisions, we integrate Explainable Artificial Intelligence (XAI) techniques–Grad-CAM, Grad-CAM++, and Score-CAM–to visualize and interpret the features contributing to the classification outcomes. Our model achieved an accuracy of 98.68%, highlighting the effectiveness of the VGG-16 architecture for this task. Each XAI method provided unique insights: Grad-CAM localized general feature regions, Grad-CAM++ offered more refined and detailed heatmaps, and Score-CAM produced clearer, gradient-free visual explanations that enhance transparency and stability. Our findings indicate that Score-CAM outperforms the other techniques by providing stable, precise heatmaps, making it particularly suitable for clinical use. This combination of high accuracy and interpretability supports the applicability of AI-driven diagnostics in real-world healthcare settings, fostering trust and reliability in automated lung disease classification.