Uncertainty-Aware and Explainable Ensemble Deep Learning Framework for Multi-Class Skin Lesion Classification

2026-08-11

AI Summary

This study addresses the challenges of multi-class skin lesion classification in dermoscopic images—namely, high intra-class variability, inter-class similarity, class imbalance, and insufficient model interpretability—by proposing a deep ensemble framework that integrates MaxViT-Tiny with multiple convolutional neural networks (ConvNeXt-Tiny and EfficientNetV2-B0). To the best of our knowledge, this is the first work to apply MaxViT-Tiny to skin lesion classification. The framework incorporates Monte Carlo Dropout to quantify prediction uncertainty and employs Grad-CAM++ for visual interpretability. Evaluated on the HAM10000 dataset, the model achieves 96% accuracy, 99% ROC-AUC, and 95% macro-averaged precision, recall, and F1-score after uncertainty-based filtering, substantially enhancing diagnostic reliability and transparency.

Abstract

Skin cancer diagnosis from dermoscopic images remains challenging due to high intra-class variability, inter-class similarity, class imbalance, and the limited interpretability of deep learning models. This paper proposes an uncertainty-aware and explainable deep learning framework for multi-class skin lesion classification. The framework combines a vision transformer model (MaxViT-Tiny) with CNN-based models (ConvNeXt-Tiny and EfficientNetV2-B0) through deep ensemble learning. Monte Carlo (MC) Dropout estimates predictive uncertainty and identifies unreliable predictions, while Grad-CAM++, an explainable AI (XAI) technique, provides visual explanations by highlighting lesion regions that influence model decisions. Evaluated on the HAM10000 dataset, the framework achieves 96% accuracy and 99% ROC-AUC under uncertainty-aware filtering (entropy < 1.0, confidence >= 0.7), with macro-average precision, recall, and F1-score of 94%, 95%, and 95%, respectively, and 96% weighted-average scores across all three metrics. The results demonstrate accurate, interpretable, and uncertainty-aware skin lesion classification for trustworthy computer-aided diagnosis.

Problem

• skin lesion classification
• class imbalance
• interpretability
• intra-class variability
• inter-class similarity

Innovation

• uncertainty-aware learning
• explainable AI
• deep ensemble learning
• vision transformer
• skin lesion classification