Multi-Scale Transformer Architecture for Accurate Medical Image Classification

TL;DR

This paper presents a multi-scale Transformer architecture that significantly improves skin lesion classification accuracy and interpretability, outperforming existing models on the ISIC 2017 dataset.

Contribution

It introduces a novel multi-scale feature fusion mechanism within a Transformer model tailored for medical image classification, enhancing global and local feature extraction.

Findings

Outperforms ResNet50, VGG19, ResNext, and Vision Transformer on key metrics

Demonstrates superior accuracy, AUC, F1-Score, and Precision

Provides interpretable Grad-CAM visualizations aligning with lesion sites

Abstract

This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medical image analysis. By integrating a multi-scale feature fusion mechanism and refining the self-attention process, the model effectively extracts both global and local features, enhancing its ability to detect lesions with ambiguous boundaries and intricate structures. Performance evaluation on the ISIC 2017 dataset demonstrates that the improved Transformer surpasses established AI models, including ResNet50, VGG19, ResNext, and Vision Transformer, across key metrics such as accuracy, AUC, F1-Score, and Precision. Grad-CAM visualizations further highlight the interpretability of the model, showcasing strong alignment between the algorithm's focus areas and actual lesion sites. This research underscores the transformative potential of advanced AI models in medical imaging, paving the way for more accurate and reliable diagnostic tools. Future work will explore the scalability of this approach to broader medical imaging tasks and investigate the integration of multimodal data to enhance AI-driven diagnostic frameworks for intelligent healthcare.