Hybrid AI Architecture Revolutionizes Skin Lesion Diagnosis: Combining ViT and LLaMA 3.2 for Interpretable Medical Image Analysis

This article proposes an innovative hybrid AI system that deeply integrates the Vision Transformer (ViT) visual model with the LLaMA 3.2 large language model. It achieves skin lesion classification on the HAM10000 dataset while generating natural language explanations to enhance diagnostic interpretability, addressing the "black box" problem of traditional deep learning models and providing a new paradigm for the clinical application of medical AI.

Skin cancer is one of the most common cancers globally, and early accurate diagnosis is crucial for prognosis. Traditional diagnosis relies on doctors' experience, but there is a shortage of specialized doctors in resource-poor areas; deep learning has great potential in medical image analysis, but its "black box" nature limits its application in high-risk medical scenarios. Balancing diagnostic accuracy and interpretability is a core issue in the field of AI in healthcare.

Vision Transformer Visual Encoding

ViT divides images into patches, captures global spatial relationships through self-attention, and better understands the long-range dependencies of lesion distribution.

LLaMA 3.2 Language Module

It undertakes semantic understanding and explanation generation, outputs classification results (e.g., melanoma) and explains the judgment basis (irregular borders, uneven color, etc.).

Synergy Mechanism

ViT extracts visual features, LLaMA converts them into professional descriptions, and end-to-end training achieves feature space alignment to leverage their respective domain advantages.

HAM10000 Dataset

It contains 10015 dermoscopic images of 7 lesion types (e.g., melanoma, benign nevi), annotated by professional doctors, and is a standard benchmark for skin lesion AI systems.

Multi-task Training

It optimizes both classification accuracy and explanation quality simultaneously, forcing the model to extract features useful for both diagnosis and explanation. Experiments show that this strategy improves classification accuracy and interpretability.

1. Enhanced Interpretability: Natural language explanations bridge the gap between AI decisions and human understanding, helping doctors verify decisions and build doctor-patient trust.
2. Auxiliary Medical Education: Explanatory texts can serve as teaching materials to help medical students learn lesion features.
3. Promoting Resource Balance: Primary healthcare institutions can use it as a virtual expert to provide preliminary screening and alleviate the problem of uneven resource distribution.

Limitations

• Only covers 7 lesion types from HAM10000, while there are more types in clinical practice;
• Explanation quality depends on annotation quality, and annotation biases affect outputs;
• Robustness needs to be verified when image quality is poor or shooting conditions vary.

Future Directions

• Incorporate multi-modal data (medical history, demographics);
• Explore few-shot learning to adapt to rare lesions;
• Establish a human-AI collaborative diagnosis process to leverage the auxiliary value of AI.