Robust Melanoma Thickness Prediction via Deep Transfer Learning enhanced by XAI Techniques

TL;DR

This paper presents a deep learning approach for predicting melanoma thickness from dermoscopy images, combining transfer learning with explainability techniques to improve accuracy and interpretability in skin cancer diagnosis.

Contribution

It introduces a novel application of transfer learning and XAI methods to enhance melanoma thickness prediction and provides insights into the data-model relationship.

Findings

Models achieved significant accuracy improvements over previous methods.

Correlation analysis revealed a moderate relationship between predictions and actual thickness.

Explainability techniques helped understand feature distinctions related to melanoma depth.

Abstract

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.