Hybrid Deep Learning System for Nail Disease Diagnosis Combining Synthetic Data, Explainable AI and Multi-Model Architectures

Abstract

ABSTRACT Nail abnormalities serve as crucial indicators of a wide range of systemic, dermatological and nutritional disorders, necessitating early and precise detection for effective medical intervention. However, conventional diagnostic methods rely on subjective visual assessments by healthcare professionals which can lead to inconsistencies and delays in diagnosis. This research presents an AI-powered Nail Abnormality Detection System which has been designed to automate the identification and classification of five common nail abnormalities which are clubbing, spoon nails (koilonychia), black nails, splinter hemorrhages and nail pitting alongside normal nails. By integrating advanced deep learning techniques, this study addresses key challenges in medical image analysis including data scarcity, model interpretability and real-time diagnostic efficiency. The proposed system utilizes a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance feature extraction by improving both classification accuracy and generalization. Given the limited availability of high-quality annotated datasets, this research also explores synthetic data generation using diffusion models to augment training data while ensuring balanced representation of various nail conditions. In addition, Explainable AI (XAI) techniques such as Grad-CAM, Ablation-CAM and SIDU, are employed to provide visual interpretability, enhancing model transparency and enabling medical professionals to validate AI-driven predictions. Furthermore, YOLO-based segmentation is integrated to precisely localize affected regions by facilitating severity assessment and aiding in clinical decision-making. Comprehensive experimental evaluations demonstrate that the system achieves great classification accuracy, high precision and recall across multiple datasets while outperforming traditional diagnostic approaches. The findings establish that the combination of deep learning and computer vision techniques significantly enhances diagnostic reliability, offering a scalable, accessible, and cost-effective solution for early disease detection. The study not only advances the field of AI-driven dermatological diagnostics but also lays the groundwork for future research in AI-assisted healthcare applications. With its ability to provide rapid, consistent and interpretable results, this system has the potential to revolutionize telemedicine, point-of-care screening and large-scale population health monitoring. The pipeline begins with a Nail-Filter CNN that screens photographs for relevance, achieving 98 % overall accuracy, 100 % recall for nail images and 100 % precision for non-nail images (F1 = 0.98), thus eliminating most irrelevant inputs before further processing. A hybrid