Have you ever hesitated before uploading a sensitive photo to a cloud-based AI service? When it comes to healthcare applications, especially skin lesion screening, privacy isn't just a feature—it's a requirement.
In this tutorial, we are going to build a high-performance, Edge AI application that performs real-time skin lesion analysis directly in the browser. By leveraging TensorFlow.js, WebAssembly (WASM), and Vision Transformers (ViT), we ensure that user data never leaves their device, achieving sub-second latency and bank-level privacy. We will explore how to deploy a quantized Vision Transformer to a React environment to bridge the gap between heavy deep learning and lightweight web experiences.
Why Edge AI for Vision Tasks? 🥑
Traditionally, Vision Transformers (ViT) were considered too "heavy" for web browsers. However, with the evolution of WebAssembly (WASM) and model quantization, we can now run complex Vision / Edge AI tasks with incredible efficiency. This approach solves three major bottlenecks:
Privacy: Zero server-side data storage.
