DEEP LEARNING-BASED NAIL DISEASE DETECTION: A COMPREHENSIVE REVIEW
Authors/Creators
- 1. ME Computer Engineering, Dept. of Computer Engineering, Matoshri Institute of Engineering and Research Centre, Savitribai Phule Pune University, Nashik, India
- 2. Associate Professor & Head, Dept. of Computer Engineering, Matoshri Institute of Engineering and Research Centre, Savitribai Phule Pune University, Nashik
Description
Abstract - In recent years, deep learning has emerged as a transformative technology in the field of
medical diagnostics, offering automated solutions for disease detection with high accuracy. While
significant progress has been made in applying these techniques to major conditions like cancer and
retinal disorders, comparatively little attention has been given to diagnostic cues visible in human
nails. 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 comprehensive
review of existing deep learning approaches for nail disease detection, analyzing key models such as
CNNs, transfer learning architectures (VGG16, DenseNet201, MobileNet), and transformer-based
networks like BEiT. A comparative analysis of methodologies, datasets, evaluation metrics, and
classification performance is provided across multiple studies. In addition, this review identifies
common 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 architecture
is proposed to consolidate best practices observed in the literature. The paper concludes by outlining
future directions including mobile-ready AI deployment, improved data diversity, and clinical
validation to enable real-world adoption of AI-assisted nail diagnostics.
Key Words: Nail disease detection, Deep learning, Convolutional Neural Networks (CNN), Transfer
learning, Mobile health (mHealth), Medical image analysis, Explainable AI (XAI), Nail image
datasets, Computer-aided diagnosis, Nail segmentation.