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

IF 1.1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Amel Laidi, Mohammed Ammar, Mostafa EL HABIB DAHO, S. Mahmoudi
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引用次数: 0

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 e ffi ciency of data augmentation using GANs for atherosclerosis, with results comparable to the state of the art. long as the original work is properly cited.
从冠状动脉CT血管造影中改进自动动脉粥样硬化筛查的GAN数据增强
简介:动脉粥样硬化是一种慢性疾病,可导致冠状动脉疾病、中风甚至心脏病发作。早期发现可导致及时干预并挽救生命。目的:在这项工作中,提出了一种基于全自动迁移学习的冠状动脉CT血管造影(CCTA)动脉粥样硬化检测模型。利用生成式对抗网络生成训练数据,提高了模型的性能。方法:采用Resnet网络在原始数据集上进行首次实验,准确率为95.2%,灵敏度为60.8%,特异性为99.25%,PPV为90.48%。然后使用生成对抗网络(GAN)生成一组新的图像来平衡数据集,创建更积极的图像。实验将100 ~ 1000张图像添加到数据集中。结果:增加1000张图像,准确率小幅下降至93.2%,但整体性能有所提高,灵敏度为89.0%,特异性为97.37%,PPV为97.13%。结论:本文是早期研究gan对动脉粥样硬化数据增强效率的研究项目之一,其结果可与最先进的水平相媲美。只要正确引用原文。
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来源期刊
EAI Endorsed Transactions on Scalable Information Systems
EAI Endorsed Transactions on Scalable Information Systems COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
2.80
自引率
15.40%
发文量
49
审稿时长
10 weeks
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