COVID-19 Detection using DAgger Active Learning

This project leverages active learning to detect COVID-19 from various data sources. The goal is to improve the accuracy and robustness of COVID-19 detection models using advanced machine learning techniques.

Table of Contents

• Introduction
• Dataset
• Installation
• Usage
• Model Training
• Evaluation
• Results
• Contributing

Introduction

The COVID-19 pandemic has necessitated rapid advancements in diagnostic tools. This project explores the use of active learning, particularly the DAgger algorithm, to enhance the detection of COVID-19 from medical images and other related data.

Dataset

The datasets used in this project include:

1. Chest X-ray images from the COVID-19 Radiography Database.
2. CT Scan images from the UCSD-AI4H COVID-CT Dataset.

Installation

To get started, clone the repository and install the necessary dependencies:

git clone https://github.com/sarojshakya01/covid-detection-using-active-learning.git
cd covid-detection-using-active-learning
pip install -r requirements.txt

Usage

After installation, you can use the notebook file to preprocess data, train models, and evaluate performance:

Model Training

The training process involves using the DAgger algorithm to iteratively refine the model. The configuration file config.yaml allows you to specify hyperparameters and other settings.

Evaluation

Model evaluation is performed on a separate test set. Metrics such as accuracy, precision, recall, and F1-score are computed to assess performance.

Results

The results of the model training and evaluation are stored in the specified directory. Detailed performance metrics and visualizations can be found in the evaluation report.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request with your changes. Ensure that your code adheres to the project's coding standards and includes appropriate tests.