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.

翻译：人类指甲疾病在所有年龄段中逐渐被观察到，尤其是在老年人群中，这类疾病常被忽视直至病情加重。早期检测与准确诊断此类病症至关重要，因为它们有时能揭示人体潜在的健康问题。但由于不同疾病类型间视觉差异细微，这一任务具有挑战性。本文提出一种基于机器学习的自动化指甲疾病分类模型，该模型基于包含六种类别、总计3,835张图像的公开数据集构建。所有图像均统一调整为224×224像素以确保一致性。为评估性能，我们训练并分析了四种经典CNN模型——InceptionV3、DenseNet201、EfficientNetV2与ResNet50。其中InceptionV3以95.57%的准确率表现最优，DenseNet201以94.79%的准确率次之。为增强模型鲁棒性并降低其在复杂或噪声图像上的误判率，我们采用了对抗训练技术。同时，为提升模型决策的可解释性，我们运用SHAP方法对预测过程中的关键特征进行可视化标注。该系统有望成为辅助医生的有效工具，使指甲疾病诊断更加精准高效。