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

EAI Endorsed Transactions on Scalable Information Systems

article

Amel Laidi¹  Mohammed Ammar¹  Mostafa El Habib Daho²  Said Mahmoudi³ 

[1] University of Boumerdes;University of Tlemcen;University of Mons

关键词: Atherosclerosis;    CCTA;    Transfer learning;    Generative Adversarial Networks;    GAN;    Data augmentation;   

DOI  :  10.4108/eai.17-5-2022.173981

学科分类：社会科学、人文和艺术（综合）

来源: Bern Open Publishing

【 摘 要 】

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.

【 授权许可】

CC BY