https://github.com/kingabzpro/dagshub-pneumonia-classification

https://github.com/kingabzpro/dagshub-pneumonia-classification

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• Host: GitHub
• URL: https://github.com/kingabzpro/dagshub-pneumonia-classification
• Owner: kingabzpro
• License: mit
• Created: 2021-10-24T11:19:53.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-10-24T12:30:52.000Z (over 3 years ago)
• Last Synced: 2025-01-17T22:12:03.846Z (4 months ago)
• Language: PureBasic
• Size: 170 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Pneumonia-Classification

### This is a Python3 (TensorFlow) implementation of Pneumonia Detection using chest X-ray image.

## The Dataset

[comment]: <> (Uncomment when streamlit is merged into master    ![](task_5_streamlit/images/Example.png))

The dataset comprises 5,863 frontal-view chest X-ray images organized into three folders - train, test, val. 

The folders are divided into sub-folders for each image category - Pneumonia and Normal. 

[The dataset](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia) is available on the Kaggle platform.

### Acknowledgements

- [Data](https://data.mendeley.com/datasets/rscbjbr9sj/2)

- License: CC BY 4.0

- [Citation](http://www.cell.com/cell/fulltext/S0092-8674(18)30154-5)

## Prerequisites

- Python 3.8+

- TensorFlow 2.5+

- All the specified requirements in the text file

## Usage

1. Clone this repository.

2. Install requirements.txt using `pip install -r requirements.txt`.

3. Use DVC to pull the files that are stored on the DAGsHub remote storage by running `dvc pull`

4. Modify the code as you wish.

5. Run `dvc repro` to run the pipeline and train the model.

**Note:** *If you are adding/removing/moving files to different directories, it can affect the DVC pipeline, and therefore

the `dvc repro` command might not run properly.*