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

Laidi, Amel; Ammar, Mohammed; Daho, Mostafa; Mahmoudi, Saïd

doi:10.4108/eai.17-5-2022.173981

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

Laidi, Amel; Ammar, Mohammed; Daho, Mostafa et al.

2023 • In EAI Endorsed Transactions on Scalable Information Systems, p. 173981

Peer Reviewed verified by ORBi

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https://hdl.handle.net/20.500.12907/44634

DOI
10.4108/eai.17-5-2022.173981

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Keywords :

General Medicine; Medical Images Analysis; GAN; Coronary CT Angiography

Abstract :

[en] 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.

Disciplines :

Computer science

Author, co-author :

Laidi, Amel

Ammar, Mohammed

Daho, Mostafa

Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle

Language :

English

Title :

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

Publication date :

15 January 2023

Journal title :

EAI Endorsed Transactions on Scalable Information Systems

eISSN :

2032-9407

Publisher :

European Alliance for Innovation n.o.

Pages :

173981

Peer reviewed :

Peer Reviewed verified by ORBi

Development Goals :

9. Industry, innovation and infrastructure

Research unit :

F114 - Informatique, Logiciel et Intelligence artificielle

Research institute :

R300 - Institut de Recherche en Technologies de l'Information et Sciences de l'Informatique
R450 - Institut NUMEDIART pour les Technologies des Arts Numériques

Data Set :

http://dx.doi.org/10.4108/eai.17-5-2022.173981

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since 12 January 2023

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