Malaria Vision

Project Overview

Malaria Vision is an innovative application designed to detect malaria using advanced deep learning techniques. The app leverages the power of neural networks and Convolutional Neural Networks (CNNs) to provide accurate and reliable disease identification. This project aims to facilitate early detection and timely treatment of malaria, potentially saving countless lives. The deep learning models were developed from scratch using TensorFlow, achieving an impressive 95% accuracy.

Table of Contents

• Features
• Technologies Used
• Installation
• Usage
• Model Training
• Contributing
• License

Features

• Accurate Disease Identification: Utilizes state-of-the-art deep learning models for precise malaria detection.
• User-Friendly Interface: Provides a seamless and intuitive user experience.
• Real-time Analysis: Quickly analyzes input data and provides instant results.

Technologies Used

• TensorFlow: For implementing the deep learning models.
• Keras: For building and training the neural networks.
• TensorBoard: For visualizing the training process and model performance.
• Neural Networks & CNNs: To enhance detection capabilities and ensure accuracy.

Installation

Prerequisites

• Python: Install Python
• TensorFlow: Install TensorFlow

Steps

1. Clone the Repository:

   git clone https://github.com/yourusername/Malaria-Vision.git
   

2. Set Up Virtual Environment:

   python -m venv venv
   source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install Dependencies:

   pip install -r requirements.txt

4. Set Up Machine Learning Model:

   • Follow the instructions in the model/ directory to set up and train the model.
   • Ensure you have the trained model file in the appropriate directory.

5. Run the Application:

   • Instructions for running the app should be added based on the specific framework or interface used.

Usage

1. Open the app interface.
2. Use the interface to upload an image of a blood smear sample.
3. The app will analyze the image and provide a diagnosis, indicating whether malaria is detected.

Model Training

1. Data Preparation:

   • Ensure you have a dataset of blood smear images, labeled for malaria presence.
   • Preprocess the images as needed (e.g., resizing, normalization).

2. Training the Model:

   • Navigate to the model/ directory.

   • This script utilizes TensorFlow and Keras to train a CNN for malaria detection.

   • Use TensorBoard to visualize the training process and monitor performance.

3. Achieving High Accuracy:

   • The model was developed from scratch and fine-tuned to achieve 95% accuracy.

4. Saving the Model:

   • After training, the model will be saved as a checkpoint.
   • Ensure the checkpoint file is stored in the appropriate directory for later use.

Contributing

We welcome contributions to improve ETVAK. Please follow these steps to contribute:

1. Fork the repository.
2. Create a new branch: git checkout -b feature-branch
3. Make your changes and commit them: git commit -m 'Add some feature'
4. Push to the branch: git push origin feature-branch
5. Submit a pull request.

License

This project is licensed under the MIT License - see the LICENSE file for details.

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