Building an AI-Powered Skin Disease Detector with Flask, TensorFlow Lite, and Groq
The author built a web app called SKIN that uses AI models to detect skin conditions, including a 7-class skin lesion classifier and a monkeypox detector. The app runs on a simple Flask backend with TensorFlow Lite for fast inference and Groq's Llama 4 Scout for generating medical explanations.
Why it matters
This project demonstrates how AI can be leveraged to provide accessible and informative skin condition detection, which could help people seek early medical attention.
Key Points
- 1Developed a web app that can classify skin lesions and detect monkeypox using AI models
- 2Utilized TensorFlow Lite for efficient model inference on a free-tier server
- 3Integrated Groq's Llama 4 Scout to generate plain-language medical explanations for predictions
- 4Kept the tech stack simple with Flask, Tailwind CSS, and Alpine.js
Details
The author built SKIN, a web application that can detect various skin conditions using AI models. The app includes a 7-class skin lesion classifier trained on the HAM10000 dataset, which can identify conditions like melanoma, basal cell carcinoma, and melanocytic nevi. It also has a separate binary classifier for detecting monkeypox lesions. The models are implemented using TensorFlow Lite, which allows for fast inference on a free-tier server. The app also integrates Groq's Llama 4 Scout language model to generate plain-language medical explanations for each prediction. The author deliberately kept the tech stack simple, using Flask for the backend, Tailwind CSS and Alpine.js for the frontend, and Chart.js for data visualization. The entire application is deployed on Render's free tier.
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