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| ## Motivation | |
| Pneumonia is a lung infection (🫁) that inflames the air sacs in one or both lungs. This infection arises when the air sacs get filled with fluid or pus (purulent material). It can be a bacterial or viral infection. The main symptoms are - cough with phlegm or pus, fever, chills, and breathing difficulty. | |
| This disease is responsible for over 15% of all deaths of children under five years old worldwide. This proves the severity of this disease and the need for accurate detection. | |
| The most commonly used method to diagnose pneumonia is through chest radiograph or chest X-ray, which depicts the infection as an increased opacity in the lungs' specific area(s). | |
| To increase the diagnosis procedure's efficacy and reach, we can leverage machine learning algorithms to identify abnormalities in the chest X-ray images. In this model, many chest X-ray images (both normal and pneumonia) are fed to build `Convolutional Neural Network (CNN)` model for fulfilling the purpose. | |
| ## Requirements | |
| - Python 3.7.x | |
| - Tensorflow 2.4.1+ | |
| - Keras 2.4.3+ | |
| - scikit-learn 0.24.1+ | |
| - matplotlib 3.3.3+ | |
| - texttable 1.6.3+ | |
| - gradio 1.5.3+ | |
| ## Dataset | |
| You can download the dataset from [kaggle](https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia/). Use the underlying download link to download the dataset. | |
| ### Instructions to follow | |
| * Extract the archive | |
| * You will find several directories in it | |
| * Copy the `chest-xray` directory contents (`train`, `test` and `val` subdirectories) to the `data` folder | |
| The number of images belonging to both classes (`Normal` and `Pneumonia`) in the `train`, `test` and `val` datasets are - | |
| <img width="326" alt="Screenshot 2021-02-07 at 16 40 00" src="https://user-images.githubusercontent.com/76659596/107151515-4083f280-6963-11eb-84c7-f2a23cc24134.png"> | |
| ## Installation | |
| * Clone the repository | |
| `git clone https://github.com/baishalidutta/Pneumonia-Detection.git` | |
| * Install the required libraries | |
| `pip3 install -r requirements.txt` | |
| ## Usage | |
| Enter into the `source` directory to execute the following source codes. | |
| * To generate the model on your own, run | |
| `python3 cnn_training_model.py` | |
| * To evaluate any dataset using the pre-trained model (in the `model` directory), run | |
| `python3 cnn_model_evaluation.py` | |
| Note that, for evaluation, `cnn_model_evaluation.py` will use all the images contained inside both `test` and `val` subdirectories (inside `data` directory). | |
| Alternatively, you can find the whole analysis in the notebook inside the `notebook` directory. To open the notebook, use either `jupyter notebook` or `google colab` or any other IDE that supports notebook feature such as `PyCharm Professional`. | |
| ## Evaluation | |
| Our model is trained with 96% accuracy on the training dataset. The model's accuracy on the `test` and `val` datasets are 91% and 88% respectively. In both cases, the `f1-score` and `ROC_AUC Score` are relatively high, as shown below. | |
| ### On Test Dataset (624 images, 234 `Normal` and 390 `Pneumonia`) | |
| <p align="center"> | |
| <img width="960" alt="Screenshot 2021-02-07 at 17 07 23" src="https://user-images.githubusercontent.com/76659596/107152321-93f83f80-6967-11eb-95b4-0bfb3ccae6d7.png"> | |
| </p> | |
| ### On Validation Dataset (16 images, 8 `Normal` and 8 `Pneumonia`) | |
| <p align="center"> | |
| <img width="960" alt="Screenshot 2021-02-07 at 17 10 07" src="https://user-images.githubusercontent.com/76659596/107152360-ba1ddf80-6967-11eb-90cb-dfaeca31f275.png"> | |
| </p> | |
| ## Web Application | |
| To run the web application locally, go to the `webapp` directory and execute: | |
| `python3 web_app.py` | |
| This will start a local server that you can access in your browser. You can either upload/drag a new X-ray image or select any test X-ray images from the examples below. | |
| You can, alternatively, try out the hosted web application [here](https://gradio.app/g/baishalidutta/Pneumonia-Detection). | |
| ## Developer | |
| Baishali Dutta (<a href='mailto:[email protected]'>[email protected]</a>) | |
| ## Contribution [](https://github.com/baishalidutta/Pneumonia-Detection/issues) | |
| If you would like to contribute and improve the model further, check out the [Contribution Guide](https://github.com/baishalidutta/Pneumonia-Detection/blob/main/CONTRIBUTING.md) | |
| ## License [](https://www.apache.org/licenses/LICENSE-2.0) | |
| This project is licensed under Apache License Version 2.0 | |