Confidence-Aware Dual-CNN Framework for Vitiligo Detection Using EfficientNet-B0 with Freeze-Unfreeze Transfer Learning

• Author(s): Dheeraj S Kumar; Rohit John Alex; Adhidev M D; Dr. K. Arthi
• Paper ID: 1717366
• Page: 1406-1412
• Published Date: 13-05-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 11 May-2026

Abstract

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.

Keywords

Vitiligo Detection, Dual CNN, EfficientNet-B0, Transfer Learning, Uncertainty Estimation, Disagreement Threshold, OpenCV, Random Forest, Medical Image Classification

Citations

IRE Journals:
Dheeraj S Kumar, Rohit John Alex, Adhidev M D, Dr. K. Arthi "Confidence-Aware Dual-CNN Framework for Vitiligo Detection Using EfficientNet-B0 with Freeze-Unfreeze Transfer Learning" Iconic Research And Engineering Journals Volume 9 Issue 11 2026 Page 1406-1412

IEEE:
Dheeraj S Kumar, Rohit John Alex, Adhidev M D, Dr. K. Arthi "Confidence-Aware Dual-CNN Framework for Vitiligo Detection Using EfficientNet-B0 with Freeze-Unfreeze Transfer Learning" Iconic Research And Engineering Journals, 9(11)