Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis

Enam Ahmed Taufik; Abdullah Khondoker; Antara Firoz Parsa; Seraj Al Mahmud Mostafa

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arXiv · 2508.04573

Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis

Enam Ahmed Taufik·Abdullah Khondoker·Antara Firoz Parsa·Seraj Al Mahmud Mostafa

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Abstract

Accurate skin disease classification is a critical yet challenging task due to high inter-class similarity, intra-class variability, and complex lesion textures. While deep learning-based computer-aided diagnosis (CAD) systems have shown promise in automating dermatological assessments, their performance is highly dependent on image pre-processing and model architecture. This study proposes a deep learning framework for multi-class skin disease classification, systematically evaluating three image pre-processing techniques: standard RGB, CMY color space transformation, and Contrast Limited Adaptive Histogram Equalization (CLAHE). We benchmark the performance of pre-trained convolutional neural networks (DenseNet201, Efficient-NetB5) and transformer-based models (ViT, Swin Transformer, DinoV2 Large) using accuracy and F1-score as evaluation metrics. Results show that DinoV2 with RGB pre-processing achieves the highest accuracy (up to 93%) and F1-scores across all variants. Grad-CAM visualizations applied to RGB inputs further reveal precise lesion localization, enhancing interpretability. These findings underscore the importance of effective pre-processing and model choice in building robust and explainable CAD systems for dermatology.

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Enam Ahmed Taufik, Abdullah Khondoker, Antara Firoz Parsa, Seraj Al Mahmud Mostafa. 2025-08-06. Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis. https://arxiv.org/abs/2508.04573

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