Comparative Analysis of Transfer Learning Models with Explainable AI for Automated Nail Disease Classification

Nail disorders change the color, texture and shape of the nail, so nail photographs are a possible input for computer aided screening. This paper compares six pretrained networks, ResNet-50, VGG16, DenseNet-121, ConvNeXt-Tiny, ViT-B/16 and DINOv2, under frozen, partial and full fine-tuning on a small, imbalanced dataset of 1,008 images in 13 classes. Images are split into five grouped folds so that near duplicates never cross folds, every configuration receives the same tuning budget on one fold, and all results come from the other four folds with group bootstrap confidence intervals. Fine-tuning beat frozen features for every network by 11.6 to 18.5 accuracy points, and partial fine-tuning matched full fine-tuning for three networks. The best Macro-F1 was 0.857 for DINOv2 with full fine-tuning, but five other configurations, including ResNet-50 with 3.6 times fewer parameters, were statistically tied with it. The backbone learning rate mattered most, and the tuning fold overstated performance by about five Macro-F1 points. Grad-CAM and attention rollout maps of the CNNs and DINOv2 were more faithful than random maps and ignored image padding, unlike ViT-B/16, and hiding watermarks lowered the predicted probability more than hiding random regions. The models are screening aids, not diagnostic tools.