Explainable depth-wise and channel-wise fusion models for multi-class skin lesion classification.

ABSTRACT: The clinical adoption of deep learning in dermatology requires models that are not only highly accurate but also transparent and trustworthy. To address this dual challenge, this study presents a systematic investigation into deep feature fusion, exploring how to effectively combine complementary representations from diverse neural network architectures. We design and rigorously evaluate six distinct fusion models, first investigating depth-wise and channel-wise strategies for integrating features from powerful Convolutional Neural Network (CNN) backbones, and subsequently incorporating the global contextual awareness of Vision Transformers (ViTs). Evaluated on the challenging 7-class HAM10000 dataset, our optimized architecture achieves a weighted average Precision, Recall, and F1 score o

SUBMITTER: AbuAlkebash H 

PROVIDER: S-EPMC12826527 | biostudies-literature | 2026

REPOSITORIES: biostudies-literature