🧠 MONKEYPOX-DETECTION-USING-MACHINE-LEARNING

Empowering swift responses to monkeypox outbreaks.

Built with the tools and technologies:

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📑 Table of Contents

• Overview
• Getting Started
  • Prerequisites
  • Installation
  • Usage
  • Testing
• 🔙 Return

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🔍 Overview

Monkeypox Detection Using Machine Learning is an innovative tool designed to enhance diagnostic accuracy for monkeypox cases through advanced algorithms.

Why Monkeypox-Detection-Using-Machine-Learning?

This project aims to revolutionize the way healthcare professionals diagnose and manage monkeypox outbreaks. The core features include:

• 🧠 Advanced Algorithms: Leverage state-of-the-art machine learning techniques for precise detection.
• 📊 Interactive Notebooks: Utilize user-friendly Jupyter Notebooks for seamless data analysis and model training.
• 📁 Data Preprocessing: Streamline the organization of image datasets for effective model training.
• ⚙️ Model Flexibility: Experiment with various architectures like CNN, ResNet50, and EfficientNetB0.
• 📈 Performance Metrics: Assess model accuracy with comprehensive evaluation tools and visualizations.
• 🌐 Open Source: Collaborate and contribute under the Apache 2.0 license, fostering community-driven development.

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🚀 Getting Started

✅ Prerequisites

This project requires the following dependencies:

• Programming Language: JupyterNotebook
• Package Manager: Pip

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🛠️ Installation

Build Monkeypox-Detection-Using-Machine-Learning from the source and install dependencies:

1. Clone the repository:

   git clone https://github.com/yogambar/Monkeypox-Detection-Using-Machine-Learning

2. Navigate to the project directory:

   cd Monkeypox-Detection-Using-Machine-Learning

3. Install the dependencies:

   pip install -r requirements.txt

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▶️ Usage

Run the project with:

python {entrypoint}

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🧪 Testing

Monkeypox-Detection-Using-Machine-Learning uses the {test_framework} test framework. Run the test suite with:

pytest

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🔙 Return

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