Research
On Impact of Loss Function on the Performance of Neural Networks in Melanoma Diagnosis
arXiv:2610.06139v1 Announce Type: cross Abstract: Melanoma is the deadliest type of skin cancer, whose early diagnosis is crucial for patients' survival. Image classification using deep learning model
arXiv:2610.06139v1 Announce Type: cross 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 this work, we have investigated the effect of different loss functions on the performance of deep neural networks. We trained these networks using focal loss, logit-adjusted softmax cross-entropy (CE) loss, and weighted softmax CE loss, and we report different metrics for evaluating performance and uncertainty calibration. Our results suggest that focal loss delivers a good combination of performance in terms of AUC and uncertainty calibration in terms of expected calibration error (ECE) simultaneously.
Related
- Medical Image Segmentation based on Deep Active Contour and Mean Curvature Loss Function
- Latent Space Analysis for Interpretable Uncertainty in Melanoma Classification
- A Comparative Evaluation of Pre-trained Convolutional Neural Networks for Melanoma Detection
Source: arXiv cs.CV | 2026-10-06