DEEP LEARNING-BASED NAIL DISEASE DETECTION: A COMPREHENSIVE REVIEW

Abstract - In recent years, deep learning has emerged as a transformative technology in the field ofmedical diagnostics, offering automated solutions for disease detection with high accuracy. Whilesignificant progress has been made in applying these techniques to major conditions like cancer andretinal disorders, comparatively little attention has been given to diagnostic cues visible in humannails. Nail abnormalities can serve as non-invasive indicators of a wide range of systemic diseases,including diabetes, psoriasis, anemia, and fungal infections. This paper presents a comprehensivereview of existing deep learning approaches for nail disease detection, analyzing key models such asCNNs, transfer learning architectures (VGG16, DenseNet201, MobileNet), and transformer-basednetworks like BEiT. A comparative analysis of methodologies, datasets, evaluation metrics, andclassification performance is provided across multiple studies. In addition, this review identifiescommon research challenges such as lack of public datasets, class imbalance, limited explainability,and absence of real-time implementation. Based on these insights, a generalized system architectureis proposed to consolidate best practices observed in the literature. The paper concludes by outliningfuture directions including mobile-ready AI deployment, improved data diversity, and clinicalvalidation to enable real-world adoption of AI-assisted nail diagnostics. Key Words: Nail disease detection, Deep learning, Convolutional Neural Networks (CNN), Transferlearning, Mobile health (mHealth), Medical image analysis, Explainable AI (XAI), Nail imagedatasets, Computer-aided diagnosis, Nail segmentation.