Toward Reliable and Explainable Nail Disease Classification: Leveraging Adversarial Training and Grad-CAM Visualization

TL;DR

This paper develops and evaluates CNN-based models for automated nail disease classification, enhancing reliability with adversarial training and interpretability with Grad-CAM visualization, achieving high accuracy on a public dataset.

Contribution

It introduces a CNN-based approach with adversarial training and visualization techniques for reliable and explainable nail disease classification.

Findings

InceptionV3 achieved 95.57% accuracy

Adversarial training improved model robustness

Grad-CAM visualization aids interpretability

Abstract

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.