对比三种图像预处理方法，发现DinoV2+RGB组合在皮肤疾病分类中表现最佳且可解释性高。
Visual Bias and Interpretability in Deep Learning for Dermatological Image Analysis
- 采用RGB、CMY和CLAHE三种预处理方式，结合多种深度模型进行系统评估。
- DinoV2配合RGB预处理达到93%准确率，各项F1分数最优。
- 通过Grad-CAM可视化实现病灶精确定位，提升诊断系统可解释性。
由于类间相似度高、类内差异大及病灶纹理复杂，皮肤疾病精准分类仍是挑战。尽管基于深度学习的计算机辅助诊断（CAD）系统在自动化皮肤病评估中展现出潜力，但其性能高度依赖图像预处理和模型架构。本研究提出一种多类别皮肤疾病分类的深度学习框架，系统评估了三种图像预处理技术：标准RGB、CMY色彩空间转换和对比度受限自适应直方图均衡化（CLAHE）。我们以准确率和F1分数为指标，对比了预训练卷积神经网络（DenseNet201、EfficientNetB5）与基于Transformer的模型（ViT、Swin Transformer、DinoV2 Large）的表现。结果显示，使用RGB预处理的DinoV2在所有变体中达到最高准确率（高达93%）和最优的F1分数。对RGB输入应用Grad-CAM可视化进一步揭示了病灶的精确定位，增强了模型的可解释性。这些发现强调了有效预处理与模型选择在构建鲁棒且可解释的皮肤科CAD系统中的重要性。
原文摘要 · Abstract (English)
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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