  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.
