XAI with Convolutional Neural Network for Detecting Nail Disease

To address data scarcity and computational constraints in medical imaging, we investigate a deep learning (DL)-based framework for automating nail disease diagnosis. A convolutional neural network (CNN) is combined with explainable artificial intelligence (XAI) techniques to enable both accurate classification and interpretable decision-making for nail diseases associated with nutritional deficiencies. Multiple CNN architectures are evaluated, achieving classification accuracies exceeding 93%. The integration of CNN and XAI provides visual explanations that enhance model transparency while maintaining strong diagnostic performance under limited training data and resource-constrained conditions. In addition, this work highlights the limitations of current XAI methods and discusses several research directions focused on reliability, vulnerability, and robustness analysis to support the trustworthy deployment of AI-assisted diagnostic systems.