Tuberculosis Vision Scan

Introduction

Tuberculosis (TB) is one of the leading causes of death worldwide. Early detection is crucial for successful treatment. This project uses deep learning to classify chest X-ray images to detect signs of tuberculosis. With an accuracy of 93.5%, this project demonstrates an efficient model for the automatic detection of TB from medical images.

Project Overview

The Tuberculosis Vision Scan project leverages the VGG16 deep learning architecture to classify X-ray images as either TB-positive or TB-negative. The project includes a complete end-to-end pipeline, from data preprocessing and model training to deployment on AWS with a robust MLOps setup.

Key Features

• Model Architecture: Implemented VGG16 for image classification, fine-tuned for tuberculosis detection, achieving an accuracy of 93.5%.

• Model Tracking & Logging: Utilized MLflow and DagsHub for tracking experiments, logging models, and managing version control, ensuring efficient monitoring and reproducibility.

• Pipeline Management: Employed DVC (Data Version Control) for orchestrating data versioning and lightweight experiment tracking, facilitating smooth data management.

• CI/CD Pipeline: Automated deployment using AWS EC2, ECR, and GitHub Actions, ensuring smooth model updates and continuous integration with Docker containers.

• Web Development: Developed a Flask web application for real-time interaction with the model, allowing users to upload medical images and receive TB detection results in a user-friendly interface.

Installation

1. Clone the repository:

   git clone https://github.com/VivekShinde7/Tuberculosis-Vision-Scan.git

2. Navigate to the project directory:

   cd Tuberculosis-Vision-Scan

3. Install the required dependencies:

   pip install -r requirements.txt

4. Setup MLflow and DVC for experiment tracking and versioning.

Workflows

1. update config.yaml
2. update secrets.yaml [optional]
3. update params.yaml
4. update the entity
5. update the configuration manager in src config
6. update the components
7. update the pipeline
8. update the main.py
9. update the dvc.yaml

Future Work

• Improve model accuracy by experimenting with other deep learning architectures (e.g., ResNet, EfficientNet).
• Implement a more robust cross-validation scheme to evaluate the model's generalizability.
• Integrate explainability tools like SHAP or LIME to visualize model decisions.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests.