SKIN - Disease Detector
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.

Upload & Analyze

Select detection model and upload image

Results typically ready in under 2 seconds

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Features - SKIN SKIN - Disease Detector
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 - SKIN SKIN - Disease Detector
How It Works

Four simple steps to comprehensive skin health analysis, powered by advanced neural networks.

01

Upload / Capture

Snap or upload a skin photo from anywhere. Works with any device, any platform.

02

AI Analyzes

Our CNN processes the image with precise segmentation and classification algorithms.

03

Get Results

Receive instant diagnosis probability, heatmap visualization, and detailed report.

04

Take Action

Consult with doctors, track over time, and reduce infection risk by up to 40%.

Testimonials - SKIN SKIN - Disease Detector
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."

GP
Prof. Genevieve Pilongo
Thesis Advisor

"Impressive capstone work. The system architecture is solid and the UI is clean. Looking forward to seeing how this evolves."

RC
Prof. Rhodessa Cascaro, DIT
CCIS Dean

"The machine learning implementation is well-executed. Good balance between model complexity and practical performance."

PC
Prof. Patrick D. Cerna
ML Course Instructor

"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly. Interesting project!"

MB
Andrei Miguel Bacaling
Beta Tester

"Helped review the methodology during defense. The research approach is thorough and the documentation is comprehensive."

CD
Christopher Josh Dellosa
Research Panelist

"Tried the beta version during testing phase. Interface is intuitive and the results came back quickly. Interesting project!"

JG
Joan Gumban
Beta Tester
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