Skin Cancer Detection

A machine learning pipeline for classifying skin lesions as cancerous or non-cancerous using dermoscopic images.

Overview

This project implements a skin cancer detection system using traditional machine learning techniques. It includes:

• Preprocessing: Removes hair and glare from images.
• Feature Extraction: Extracts 273 features (color, texture, shape).
• Modeling: Trains an ensemble of SVM and Random Forest models.
• Evaluation: Provides metrics like accuracy, ROC-AUC, and visualizations.

The project uses the Skin Cancer: Malignant vs. Benign dataset. Results are detailed in docs/paper.pdf.

Installation

1. Clone the repository:

   git clone https://github.com/ahmed-226/Skin-Cancer-Detection.git
   cd Skin-Cancer-Detection
   

2. Install dependencies:

   pip install -r requirements.txt
   

3. Use:

   jupyter notebook notebooks/main.ipynb

   or

   python src/main.py