In this project, we train a deep learning model that detects viral pneumonia based on chest X-rays. The input is an image-like data and the task is a computer-vision subject.

The input data contains two types of classes of images, Pneumonia, and normal. These are the two categories of X-rays that the model will learn and predict. Also, having a relatively important size of data, we decide to use it to train a Convolutional Neural Network from scratch. Some data augmentation techniques are used to increase the size of the dataset.
The notebook notebooks/cnntrainingcolab.ipynb contains the main code to train the deep learning model.

• Context of the subject
• Data

clone the repository, create a virtual environment and install the dependencies.
For Windows users, you can run the following commands:

• git clone https://github.com/deepanshu-yadav/Pneumonia_Classifier.git
• python -m virtualenv .venv
• .venv\Scripts\activate
• pip install -r .\requirements.txt

The trained CNN is saved in a cnn_model.h5 file that can be used to test some X-ray images.