This study focuses on analyzing dermoscopy images to determine the depth of melanomas, which is a critical factor in diagnosing and treating skin cancer. The Breslow depth, measured from the top of the granular layer to the deepest point of tumor invasion, serves as a crucial parameter for staging melanoma and guiding treatment decisions. This research aims to improve the prediction of the depth of melanoma through the use of machine learning models, specifically deep learning, while also providing an analysis of the possible existance of graduation in the images characteristics which correlates with the depth of the melanomas. Various datasets, including ISIC and private collections, were used, comprising a total of 1162 images. The datasets were combined and balanced to ensure robust model training. The study utilized pre-trained Convolutional Neural Networks (CNNs). Results indicated that the models achieved significant improvements over previous methods. Additionally, the study conducted a correlation analysis between model's predictions and actual melanoma thickness, revealing a moderate correlation that improves with higher thickness values. Explainability methods such as feature visualization through Principal Component Analysis (PCA) demonstrated the capability of deep features to distinguish between different depths of melanoma, providing insight into the data distribution and model behavior. In summary, this research presents a dual contribution: enhancing the state-of-the-art classification results through advanced training techniques and offering a detailed analysis of the data and model behavior to better understand the relationship between dermoscopy images and melanoma thickness.

该研究通过使用深度学习模型，分析皮肤镜图像以确定黑素瘤的深度，从而提高黑素瘤深度的预测，并提供了与深度相关的图像特征的存在性分析。

深度迁移学习与XAI技术增强的鲁棒黑素瘤厚度预测