The COVID-19 pandemic is severely impacting the health and wellbeing of countless people worldwide. Early detection of infected patients is a crucial first step in controlling the disease, which can be achieved through radiography, according to prior literature that shows COVID-19 causes chest abnormalities noticeable in chest x-rays.
Deep Codi learns these abnormalities and is able to accurately predict whether a patient is infected with coronavirus based on the patient’s chest x-ray. Codi is an effective diagnosis tool that has immediate downstream effects in clinical settings and in the field of radiology.
The data folder is omitted from the git repo since it is large. For clarity and consistency, the folder structure is:
|code
|data
|--main_dataset
|--test
|--1_covid
|--0_non
|--Atelectasis
|--Cardiomegaly
|--Consolidation
|--Edema
|--Enlarged_Cardiomediastinum
|--Fracture
|--Lung_Lesion
|--Lung_Opacity
|--No_Finding
|--Pleural_Other
|--Pneumonia
|--Pneumothorax
|--Support_Devices
|--train
|--1_covid
|--0_non
Where main_dataset
has been renamed from the original folder data_upload_v2
, as additional data sets may be added at a later point.
The main_dataset
can be found here. Additionally the covid and non folders were renamed to have their class labels with an underscore in front of their names.
Documents submitted for this project are conveniently linked here:
All three models must be run with a python 3.6+ installation or virtual environment with all of the modules in requirements.txt installed.
To run the training and testing for the hand written VGG-like model just run "python main_not_pretrained.py"
Stock VGG with random weights and the transfer learning VGG model are run slightly differently.
Stock VGG is run with the "python stock.py <train/test>" for training or testing respectively.
Transfer learning VGG is run with the "python main.py <train/test>" for training or testing respectively.
Please feel free to add / edit, or contact us for more details.