Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation

• URL: http://arxiv.org/abs/2506.10302v1
• Date: Thu, 12 Jun 2025 02:29:16 GMT
• Title: Uncertainty-Aware Deep Learning for Automated Skin Cancer Classification: A Comprehensive Evaluation
• Authors: Hamzeh Asgharnezhad, Pegah Tabarisaadi, Abbas Khosravi, Roohallah Alizadehsani, U. Rajendra Acharya,
• Abstract summary: We present a comprehensive evaluation of deep learning-based skin lesion classification using transfer learning and uncertainty quantification (UQ) on the HAM10000 dataset.<n>Results show that CLIP-based vision transformers, particularly LAION CLIP ViT-H/14 with SVM, deliver the highest classification performance.<n>This study highlights the importance of integrating UQ into DL-based medical diagnosis to enhance both performance and trustworthiness in real-world clinical applications.
• Score: 11.342661330086921
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Accurate and reliable skin cancer diagnosis is critical for early treatment and improved patient outcomes. Deep learning (DL) models have shown promise in automating skin cancer classification, but their performance can be limited by data scarcity and a lack of uncertainty awareness. In this study, we present a comprehensive evaluation of DL-based skin lesion classification using transfer learning and uncertainty quantification (UQ) on the HAM10000 dataset. In the first phase, we benchmarked several pre-trained feature extractors-including Contrastive Language-Image Pretraining (CLIP) variants, Residual Network-50 (ResNet50), Densely Connected Convolutional Network (DenseNet121), Visual Geometry Group network (VGG16), and EfficientNet-V2-Large-combined with a range of traditional classifiers such as Support Vector Machine (SVM), eXtreme Gradient Boosting (XGBoost), and logistic regression. Our results show that CLIP-based vision transformers, particularly LAION CLIP ViT-H/14 with SVM, deliver the highest classification performance. In the second phase, we incorporated UQ using Monte Carlo Dropout (MCD), Ensemble, and Ensemble Monte Carlo Dropout (EMCD) to assess not only prediction accuracy but also the reliability of model outputs. We evaluated these models using uncertainty-aware metrics such as uncertainty accuracy(UAcc), uncertainty sensitivity(USen), uncertainty specificity(USpe), and uncertainty precision(UPre). The results demonstrate that ensemble methods offer a good trade-off between accuracy and uncertainty handling, while EMCD is more sensitive to uncertain predictions. This study highlights the importance of integrating UQ into DL-based medical diagnosis to enhance both performance and trustworthiness in real-world clinical applications.

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