用对抗训练提升指甲病分类模型可靠性，结合可视化解释决策过程。
Toward Reliable and Explainable Nail Disease Classification: Leveraging Adversarial Training and Grad-CAM Visualization
- 采用对抗训练增强模型在模糊或噪声图像下的鲁棒性。
- InceptionV3模型在3835张图片上达到95.57%准确率，最优表现。
- 结合SHAP分析关键特征，提升诊断结果可解释性，辅助医生决策。
人类指甲疾病在各年龄段逐渐显现，尤其老年人群中更常见，常被忽视直至病情加重。早期检测与准确诊断至关重要，因部分病症可能反映全身健康问题。然而，由于不同病种间视觉差异细微，诊断颇具挑战。本文基于公开数据集（含3,835张图像，涵盖六类指甲疾病）构建了基于机器学习的自动化分类模型。所有图像统一缩放至224x224像素。评估了四种主流CNN模型：InceptionV3、DenseNet201、EfficientNetV2和ResNet50。其中InceptionV3表现最佳，准确率达95.57%，紧随其后的是DenseNet201（94.79%）。为提升模型对复杂或噪声图像的判别能力，引入对抗训练。同时采用SHAP方法可视化关键判别特征，增强模型决策可解释性。该系统有望作为医生辅助工具，提升指甲病诊断的准确性与效率。
原文摘要 · Abstract (English)
Human nail diseases are gradually observed over all age groups, especially among older individuals, often going ignored until they become severe. Early detection and accurate diagnosis of such conditions are important because they sometimes reveal our body's health problems. But it is challenging due to the inferred visual differences between disease types. This paper presents a machine learning-based model for automated classification of nail diseases based on a publicly available dataset, which contains 3,835 images scaling six categories. In 224x224 pixels, all images were resized to ensure consistency. To evaluate performance, four well-known CNN models-InceptionV3, DenseNet201, EfficientNetV2, and ResNet50 were trained and analyzed. Among these, InceptionV3 outperformed the others with an accuracy of 95.57%, while DenseNet201 came next with 94.79%. To make the model stronger and less likely to make mistakes on tricky or noisy images, we used adversarial training. To help understand how the model makes decisions, we used SHAP to highlight important features in the predictions. This system could be a helpful support for doctors, making nail disease diagnosis more accurate and faster.
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