nirtuttnauer/covid-detector-in-chest-x-ray

Chest X-Ray Pneumonia and Covid-19 Detection

This repository contains projects focused on detecting pneumonia and Covid-19 using chest X-ray images. The projects utilize deep learning techniques to classify images and detect anomalies effectively.

Table of Contents

• Project Overview
• Dataset
• Installation
• Usage
• Methodology
• Results
• Contributing
• License

Project Overview

The main goal of these projects is to develop machine learning models that can accurately detect pneumonia and Covid-19 from chest X-ray images. The models are trained on datasets of pediatric chest X-ray images and are evaluated for their diagnostic accuracy.

Dataset

The datasets used in these projects contain X-ray images categorized into pneumonia, normal, and Covid-19 cases, organized in train, test, and val folders. The datasets are sourced from reputable medical centers and are publicly available for research purposes.

• Pneumonia Dataset:
  • Source: Kaggle Dataset
  • Additional Data: Mendeley Data
• Covid-19 Dataset:
  • Source: Cell Full Text

Installation

To run the notebooks and reproduce the results, follow these steps:

1. Clone the repository:

   git clone https://github.com/nirtuttnauer/chest-xray-detection.git
   cd chest-xray-detection

2. Create a virtual environment and install the required packages:

   python3 -m venv env
   source env/bin/activate
   pip install -r requirements.txt

3. Download the datasets and place them in the appropriate directory:

   mkdir data
   # Move the downloaded datasets into the data directory

Usage

To run the analysis, open the Jupyter notebooks and execute the cells in order:

1. train.ipynb: This notebook contains the training process for the pneumonia and Covid-19 detection models.
2. test.ipynb: This notebook contains the testing and evaluation process for the trained models.

Methodology

Pneumonia Detection

1. Data Preprocessing: Quality control screening, image resizing, and normalization.
2. Model Training: Using convolutional neural networks (CNN) to train the model on the X-ray images.
3. Evaluation: Assessing the model's performance using accuracy, precision, recall, and F1-score metrics.

Covid-19 Detection

1. Data Splitting: Dividing the dataset into training, validation, and test sets.
2. Data Augmentation: Applying techniques such as rotations, horizontal flipping, and zooming to improve generalization.
3. Model Architecture: Implementing binary and multiclass classification models using CNNs and transfer learning with VGG16.
4. Anomaly Detection: Utilizing autoencoders for unsupervised learning to detect anomalies in chest X-ray images.
5. Embedding and KNN: Extracting embeddings from the multi-class model and using k-Nearest Neighbors for classification.

Results

Pneumonia Detection

The trained model achieves a high accuracy in detecting pneumonia from chest X-ray images. Detailed results and visualizations can be found in the test.ipynb notebook.

Covid-19 Detection

The models for Covid-19 detection showed significant performance, with binary classification achieving over 89% accuracy and multi-class classification reaching 82% accuracy. Anomaly detection and KNN classification further enhanced the robustness of the system.

Contributing

Contributions are welcome! Please fork the repository and submit a pull request for any improvements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

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