Multi-Illumination Nail Analysis With Uncertainty-Aware Deep Learning: A Comprehensive Approach to Automated Fungal Detection

This study presents a comprehensive approach to nail condition classification using multi-illumination imaging and uncertainty-aware deep learning. We developed a diagnostic system capturing nail images under ten lighting conditions (normal and polarized versions of white, UV, red, green, and blue light) and classifying them into three categories: normal, fungal infections, and other pathologies. Our ConvNeXt-based neural network architecture demonstrated exceptional performance for fungal detection, achieving accuracies ranging from 93.63% to 96.31% across the various illumination modes. Multi-illumination analysis revealed distinctive signatures, with white light channels demonstrating highest importance (32–33% of attention weights) and red light showing specific utility for fungal detection (11.1% vs. 5–7% for other conditions). Spatial analysis through Gradient-weighted Class Activation Mapping (Grad-CAM) identified that models focus primarily on nail edges when detecting fungal infections, with quantitative grid analysis showing edge regions contributing approximately 3 times more to classification decisions than central regions. Bayesian ensembles provided reliable uncertainty: predictive entropy discriminates correct vs. incorrect decisions (AUROC 0.9429), with strong calibration for the fungus classifier (Brier 0.040, ECE 0.032). t-SNE visualization demonstrated clear feature space separation, with fungal infections showing the most cohesive clustering corresponding to their distinctive illumination-spatial patterns. These convergent findings support clinical examination optimization: prioritizing nail edges under white light, with supplementary red light for suspected fungal cases, represents a promising strategy for future clinical validation.