GAN Data Augmentation for Improved Automated Atherosclerosis Screening from Coronary CT Angiography

Authors

DOI:

https://doi.org/10.4108/eai.17-5-2022.173981

Keywords:

Atherosclerosis, CCTA, Transfer learning, Generative Adversarial Networks, GAN, Data augmentation

Abstract

INTRODUCTION: Atherosclerosis is a chronic medical condition that can result in coronary artery disease, strokes, or even heart attacks. early detection can result in timely interventions and save lives.
OBJECTIVES: In this work, a fully automatic transfer learning-based model was proposed for Atherosclerosis detection in coronary CT angiography (CCTA). The model’s performance was improved by generating training data using a Generative Adversarial Network.
METHODS: A first experiment was established on the original dataset with a Resnet network, reaching 95.2% accuracy, 60.8% sensitivity, 99.25% specificity and 90.48% PPV. A Generative Adversarial Network (GAN) was then used to generate a new set of images to balance the dataset, creating more positive images. Experiments were made adding from 100 to 1000 images to the dataset.
RESULTS: adding 1000 images resulted in a small drop in accuracy to 93.2%, but an improvement in overall performance with 89.0% sensitivity, 97.37% specificity and 97.13% PPV.
CONCLUSION: This paper was one of the early research projects investigating the efficiency of data augmentation using GANs for atherosclerosis, with results comparable to the state of the art.

Downloads

Published

17-05-2022

How to Cite

1.
Laidi A, Ammar M, Daho MEH, Mahmoudi S. GAN Data Augmentation for Improved Automated Atherosclerosis Screening from Coronary CT Angiography. EAI Endorsed Scal Inf Syst [Internet]. 2022 May 17 [cited 2022 Dec. 3];10(1):e4. Available from: https://publications.eai.eu/index.php/sis/article/view/1027