Lung Disease Detection from X-ray Images

Overview

Implementation of Deep Learning models that can identify COVID-19, Lung Opacity, Viral Pneumonia or nothing(if patient is healthy) from lung X-Ray images.

Manual

Installation instructions

In order to run the experiments in your local machine you should do the following steps.

1. Clone the repo by running git clone https://github.com/ManosL/XRay-Lung-Disease-Detection.git
2. Afterwards, install virtualenv in pip3(if you did not do that already) by running pip3 install virtualenv
3. Then move to this repository directory.
4. Then create and activate the virtual environment by running the following commands

virtualenv <venv_name>
source bin/activate

5. Afterwards, install the requirements by running pip3 install -r requirements.txt
6. Finally, download the COVID-19 Radiography Database in order to have the dataset in order to run experiments.
7. You are ready to move to code/ directory and run the experiments and demo programs!

Experiments instructions

In order to run the experiments done in order to write the report, go into code/ directory and run the following command(you can add -h to see how you should run it):

        python3 main.py --dataset_path <dataset_path> --epochs <epochs>
                        --batch_size <batch_size> --learning_rate <learning_rate>
                        --patience <patience> --pretrained_path <prerained_model>
                        --mask_images --log <log_file>

where:

1. <dataset_path>: Path to COVID-19 Radiography Dataset.
2. : Number of training epochs.
3. <batch_size>: Training batch size.
4. <learning_rate>: Learning rate of the optimizer used during training.
5. : Patience of the model, i.e how many epochs will the model wait during training to get better Validation Performance from the best epoch.
6. <pretrained_model>: Path to state dict of a model that was trained before in order to use those weights at start from training.
7. --mask_images: If set we apply masking to images.
8. <log_file>: Path to redirect stdout

Then you just follow the the output tp just specify the model that you want experiments with.

WARNING: This will take time in order to complete.

While running this program you will see logs in terminal and the graphs, will be opened in browser.