Vitiligo is a chronic depigmentation disorder affecting approximately 1–2% of the global population, and its accurate automated detection remains a challenging task due to variations in lesion size, skin tone, and imaging conditions. Existing deep learning-based vitiligo classifiers produce binary predictions without any measure of confidence or uncertainty, which can be clinically misleading. This paper proposes a Confidence-Aware Dual-CNN framework that employs two EfficientNet-B0 models with architecturally distinct classification heads, trained using a freeze-unfreeze transfer learning strategy. When both models produce consistent predictions, the system outputs a confident diagnosis of either Healthy or Vitiligo. When the models disagree beyond a designed disagreement threshold, the case is flagged as Uncertain and referred to a dermatologist for manual review. This is the first vitiligo detection framework to incorporate disagreement-based clinical uncertainty estimation. Evaluated on a dataset of 3,628 images, Model A achieves 94.98% accuracy, Model B achieves 96.28% accuracy, and the Dual-CNN framework achieves 97.82% accuracy on confident predictions, surpassing the existing IEEE CNN Autoencoder baseline of 90.16%. Supporting modules include an OpenCV-based lesion segmentation pipeline for progression tracking and a Random Forest model for treatment recommendation achieving 90% accuracy with a macro F1-score of 0.87.
Kumar et al. (Wed,) studied this question.