On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis

Excerpt

arXiv:2610.06139v1 Announce Type: new Abstract: Melanoma is the deadliest type of skin cancer, whose early diagnosis is crucial for patients' survival. Image classification using deep learning models has shown promising results for melanoma diagnosis. However, the performance of these models on the melanoma datasets such as SIIM-ISIC melanoma classification dataset is a challenge due to the class imbalance. One of the methods to deal with this challenge is using loss function modifications. In t