Multi-Model CNN Detection
Reduce Skin Cancer Infections with AI Precision
Powered by Convolutional Neural Networks — Detect skin cancer lesions or Monkeypox from any photo, anywhere. Up to 95% accuracy.
3 of 3 free analyses remaining
Upload & Analyze
Select detection model and upload image
Features
Powerful AI Detection
at Your Fingertips
Our CNN-powered platform provides comprehensive skin analysis tools designed for accuracy and ease of use.
Instant Upload & Analysis
Capture skin image via app, web, or post directly from social media. No app download needed.
CNN-Powered Detection
Trained on 100k+ images for melanoma, basal cell, and more. Multi-layer segmentation with heatmap visualization.
Risk Score & Report
Personalized report with severity assessment, probability scores, and recommended next steps.
Post Anywhere
Share photos seamlessly from Instagram, Facebook, WhatsApp, or email — analysis works from any platform.
Real-Time Results
Process in under 2 seconds on-device or cloud. Instant feedback when you need it most.
Track Progress
Monitor lesions over time, see improvement graphs, and share progress with your healthcare provider.
How It Works
Five Simple Steps to
Skin Health Analysis
CNN classification combined with GroqCloud AI explanation — fast, accurate, and easy to understand.
Upload / Capture
Snap or upload a skin photo from anywhere. Works with any device, any platform.
AI Analyzes
Our CNN processes the image with precise segmentation and classification algorithms.
AI Explanation
GroqCloud Llama 4 Scout generates a plain-language medical explanation of the detected condition in under 1 second.
Get Results
Receive instant diagnosis probability and confidence score with a detailed breakdown.
Take Action
Consult with doctors, track over time, and reduce infection risk by up to 40%.
Testimonials
Trusted by our community
Real feedback from advisors, researchers, and early users who helped shape SKIN.
"This project shows real promise for public health applications. The team did excellent work integrating CNN models for practical use."
"Impressive capstone work. The system architecture is solid and the UI is clean. Looking forward to seeing how this evolves."
"The machine learning implementation is well-executed. Good balance between model complexity and practical performance."
"This project shows real promise for public health applications. The team did excellent work integrating CNN models for practical use."
"Impressive capstone work. The system architecture is solid and the UI is clean. Looking forward to seeing how this evolves."
"The machine learning implementation is well-executed. Good balance between model complexity and practical performance."
"Solid proof of concept. The deployment on Render works smoothly, and the code quality shows good software engineering practices."
"Helped review the methodology during defense. The research approach is thorough and the documentation is comprehensive."
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly. Interesting project!"
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly."
"Solid proof of concept. The deployment on Render works smoothly, and the code quality shows good software engineering practices."
"Helped review the methodology during defense. The research approach is thorough and the documentation is comprehensive."
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly. Interesting project!"
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly."
"This project shows real promise for public health applications. The team did excellent work integrating CNN models for practical use."
"Impressive capstone work. The system architecture is solid and the UI is clean. Looking forward to seeing how this evolves."
"The machine learning implementation is well-executed. Good balance between model complexity and practical performance."
"This project shows real promise for public health applications. The team did excellent work integrating CNN models for practical use."
"Impressive capstone work. The system architecture is solid and the UI is clean. Looking forward to seeing how this evolves."
"The machine learning implementation is well-executed. Good balance between model complexity and practical performance."
"Solid proof of concept. The deployment on Render works smoothly, and the code quality shows good software engineering practices."
"Helped review the methodology during defense. The research approach is thorough and the documentation is comprehensive."
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly. Interesting project!"
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly."
"Solid proof of concept. The deployment on Render works smoothly, and the code quality shows good software engineering practices."
"Helped review the methodology during defense. The research approach is thorough and the documentation is comprehensive."
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly. Interesting project!"
"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly."