Skin-Lesion-Analyzer-Webapp

A modern web application for classifying skin lesions into various diagnostic categories using deep learning and Streamlit.

Features

• Instant Classification: Upload dermatoscopic images for immediate diagnostic suggestions.
• Visual Analytics: Detailed visualization of model predictions and confidence levels.
• Database Integration: Securely logs analysis history for longitudinal tracking.
• Responsive Design: Optimized for both desktop and mobile dermatoscopy workflows.

Tech Stack

• Language: Python 3.8+
• Framework: Streamlit, PyTorch
• Libraries: OpenCV, Pandas, Matplotlib

Project Structure

├── app.py              # Main Streamlit application entry point
├── src/                # Core logic (inference, DB, visualization)
├── data/               # Metadata and sample clinical data
├── csvfile/            # Additional CSV data storage
└── requirements.txt    # Project dependencies

Getting Started

Prerequisites

• Python 3.8+
• Streamlit

Installation

git clone https://github.com/DinhLucent/skin-classification-webapp.git
cd skin-classification-webapp
pip install -r requirements.txt

Usage

streamlit run app.py

Demo

The web interface allows users to upload images and see real-time predictions with confidence heatmaps.

License

MIT License — see LICENSE

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Built by DinhLucent