LesionAid: Vision Transformers-based Skin Lesion Generation and Classification

Synthetic data generation addresses real healthcare disparities: The framework uses AI-generated synthetic images to balance underrepresented skin lesion types in training data, helping ensure the system works fairly across all patient populations rather than performing better for common conditions only.

Explainability is built into the diagnostic process: The system incorporates explainable AI (XAI) techniques like activation maps that show why the model classified a lesion as a particular type, making it possible for doctors to verify the AI's reasoning rather than treating it as a "black box."

Mobile accessibility could democratize early detection: By enabling real-time skin lesion analysis through smartphone cameras, the technology could allow patients and doctors in resource-limited settings to perform preliminary screening without specialized dermoscopic equipment, potentially catching skin cancer earlier.

Technical design prioritizes practical deployment: The framework is optimized for "edge computing systems," meaning the AI can run directly on mobile devices rather than requiring cloud servers, which improves privacy, reduces latency, and works in areas with limited internet connectivity.

Class imbalance solutions matter for rare conditions: Using Vision Transformers-based GANs to generate synthetic training data for underrepresented lesion types helps prevent the model from being biased toward common diagnoses, a critical consideration for rare but serious skin cancers.