Masters Thesis

Proposal: Investigating the uses of Generative Adversarial Networks and optimal architecture for use in data generation for training models to diagnose COVID 19

completed as part of Masters of Science in Computing in Artificial Intelligence Research in Atlantic Technological University Letterkenny, under the supervision of Dr. Paul Greaney

What is this project

This project was part of attaining a 60 credit MSc in Artificial Intelligence Research. This project aims to investigate the possibility of using artificially generated data generated by a Generative Adversarial Network(GAN) to train a Convolutional Neural Network(CNN). Due to data shortage there have been many issues regarding the ability of current CNN models to generalize when diagnosing COVID-19 from X-rays when using a CNN, to combat this I have decided to investigate GANs to generate synthetic data to increase a model's abilitiy to generalize. I also investigate transfer learning for automated COVID-19 diagnosis in this project and the results attained when using transfer learning with an augmented dataset.

Structure of Repo

• COVID-19 Chest X-Ray - contains images for Covid 19 chest x-ray dataset.
• COVID-19_Radiography_Dataset - contains images for COVID-19_Radiography_Dataset
• Jupyter Notebooks - contains two subfolders GANs and CNNs which contain CNNs and GANs for each dataset
• Literature Review Papers - contains literature reviewed in the thesis
• Records of Supervision - Contains formal records of supervision which detail discussions between myself and my supervisor
• Thesis Reviews - Reviewed copies of my thesis where changes needed to be made
• Thesis - contains unzipped source of thesis
• ThesisImages - contains images used in my thesis
• xray_dataset_covid19 - contains images for xray_dataset_covid19
• ReadMe.md - this file you are reading detailing the project, it's purpose, and explaining it.
• Thesis.pdf - Compiled PDF file of Thesis
• Thesis.tex.zip - zipped source of Thesis

Sources for datasets:

• Radiography dataset: https://www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database
• COVID chest X-ray dataset: https://www.kaggle.com/datasets/bachrr/covid-chest-xray?datasetId=548681
• COVID-19 X-Ray dataset: https://www.kaggle.com/datasets/khoongweihao/covid19-xray-dataset-train-test-sets
• Extensive COVID-19 dataset: https://data.mendeley.com/datasets/8h65ywd2jr/3

NOTES

• The papers within the literature review folder were not written by me but by their respective authors

• Many of the images within the thesis images folder were not created by me but belong to their respective creators(cited in thesis)

• Datasets were not compiled by me original sources cited in thesis - also above in the sources for datasets section of this ReadMe

• Extensive X-Ray dataset was too large to upload to this repo please use the link mentioned in sources for datasets

• Due to limitations with Git LFS I was unable to save the GAN models to this repository will include link to drive at a later time.

• Link to GAN Models Google Drive: https://drive.google.com/drive/folders/1-uTalRnB5MiZk5jsh4TDGxclDqvoJUaN?usp=sharing

• Link to CNN Models Google Drive: https://drive.google.com/drive/folders/1bBrI8bLj5-YWCqaNWRShTPkWvDhM1Ut6?usp=sharing