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

## TL;DR

This paper introduces explainable AI models for classifying skin lesions, combining CNNs and Vision Transformers to achieve high accuracy and transparency.

## Contribution

A novel fusion framework combining depth-wise and channel-wise strategies with explainability for multi-class skin lesion classification.

## Key findings

- The optimized fusion model achieves 90% weighted average Precision, Recall, and F1 score on the HAM10000 dataset.
- Explainable AI analysis shows the model's predictions align with key dermatological features like border irregularity and color variegation.

## 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 of 90%, demonstrating superior diagnostic performance. Crucially, our comprehensive explainable AI (XAI) analysis using Grad-CAM and SHAP reveals that the fusion strategy directly dictates the model’s clinical interpretability; our most effective models learn to base their predictions on salient dermatological features, such as border irregularity and color variegation, in a manner that aligns with expert reasoning. This work provides a robust framework and valuable architectural insights for developing the next generation of high-performing, clinically reliable, and transparent AI-powered diagnostic tools.