Lung-Disease-ML-Diagnosis

Description:
A machine learning project aimed at aiding early diagnosis of lung diseases using anonymized medical image datasets. This project reduces the diagnostic workload for physicians through accurate disease classification.

Table of Contents

• Installation
• Usage
• Dataset Information
• DINOv2 Model Information
• VGG16 Model Information
• Running on Kaggle
• Development
• Contributing
• License

Installation

1. Clone the Repository:

   Clone the repository to your local machine:

   git clone https://github.com/your-username/Lung-Disease-ML-Diagnosis.git

2. Python Virtual Environment Setup:Create a virtual environment using Python 3.12.0:

   python3.12 -m venv venv

3. Activate the Virtual Environment

   • On Windows:

     venv\Scripts\activate

   • On macOS/Linux:

     source venv/bin/activate

4. Install Dependencies:Install the necessary packages using the 'requirements.txt' file: bash pip install -r requirements.txt

Usage

1. Ensure that the virtual environment is activated.
2. Run the desired script to analyze and classify lung disease images using DINOv2 models.

Dataset Information

Dataset Link

About Dataset
Artificial Intelligence has evolved a lot and is currently able to solve problems that are very complex and require human specialization. One such area is healthcare.

A lot of research happens every day to use deep learning for the betterment of humanity, and one such is healthcare.

DINOv2 Model Information

DINOv2, developed by Meta AI, is an advanced deep learning model designed for self-supervised learning. This project uses DINOv2 models to perform efficient image analysis and classification for the anonymized lung disease datasets.

To learn more about DINOv2 models, you can refer to Meta AI's official documentation here.

VGG16 Model Information

The VGG16 model, developed by the Visual Graphics Group at the University of Oxford, is a deep convolutional neural network known for its effectiveness in image recognition tasks. This project adapts VGG16 for a specific application in classifying lung disease from image data.

The original VGG16 model is renowned for its simplicity and depth, utilizing multiple 3x3 convolutional layers stacked on top of each other in increasing depth. Our implementation leverages a pretrained version of VGG16, fine-tuning it for the specific task of lung disease classification from chest radiographs.

To learn more about VGG models, you can refer to Pytorch's official documentation here.

Running on Kaggle

For GPU acceleration and more computational power, you can execute the project on Kaggle:

1. Download the Repository:
   Upload the project files or clone the repository to your Kaggle notebook environment.

2. Setup Environment:
   Create and activate a Python virtual environment:

   !python -m venv venv
   !source venv/bin/activate

3. Install Dependencies:
   Install the required libraries:

   !pip install -r requirements.txt

4. Run Analysis:
   Execute the provided analysis scripts to analyze the anonymized medical image datasets.

Development

For development purposes, ensure that your environment is set up according to the instructions provided above. Always create new branches for feature additions or bug fixes, and submit pull requests with clear and concise descriptions of your changes.

Contributing

Contributions to this project are welcome! Please open an issue or submit a pull request if you'd like to suggest improvements, add features, or fix bugs.

License

This project is licensed under the MIT License.