Revolutionize Skin Health DermaIQ: AI-Powered Skin Health Assistant
Understanding and caring for skin health shouldn’t mean endless guessing or unreliable advice. DermaIQ changes the game. By combining computer vision with an intelligent AI assistant, it delivers accurate skin condition detection and personalized guidance in one seamless experience. Upload photos, get clear results, and receive tailored advice — all in a private, user-friendly platform.
Detect Accurately
Custom-trained computer vision model identifies 10 skin conditions.
Get Personalized Advice
AI assistant “Alex” provides tailored tips, treatments, and answers.
Track Progress
Interactive dashboard stores scans and tracks skin health over time.
Enjoy Privacy
A secure, user-friendly platform ensures results remain private.
Technical Architecture
Web Interface
Django was adopted as the primary framework to provide a secure and scalable foundation for the platform. Its modular design enabled seamless integration of computer vision models, allowing AI-driven analyses to be incorporated directly into the application’s workflow. By consolidating model inference, data handling, and user interaction within a single environment, Django ensured a streamlined and reliable delivery of insights to end users.
Deep Learning Framework
TensorFlow was adopted as the core framework for developing and training models to analyze dermatological images. Its mature ecosystem enabled scalable model development, ensuring reliable detection across a wide range of skin conditions.
ROI Detection
Ultralytics’ YOLO was employed to perform region of interest (ROI) extraction, isolating the face from user-submitted images before processing. This ensured that only relevant areas were forwarded to the computer vision models, improving accuracy and efficiency in subsequent analysis.
Orchestration Layer
LangChain was utilized to coordinate interactions between different AI components and the user interface. This streamlined the integration of natural language processing, allowing the platform to present diagnostic insights in an accessible format.
Conversational Intelligence
Google’s Gemini model was incorporated to enhance natural language understanding and reasoning. Its advanced capabilities enabled the platform to provide contextually relevant explanations and personalized recommendations.
Data Storage
MySQL served as the primary relational database, providing a reliable and structured environment for storing user information, image metadata, and analysis results. Its stability and scalability supported secure management of sensitive medical data.
