Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT Angiography

Amel Laidi; Mohammed Ammar; Mostafa El Habib Daho; Said Mahmoudi

doi:10.2174/1573405619666221221092933

Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT Angiography

Curr Med Imaging. 2022 Dec 21. doi: 10.2174/1573405619666221221092933. Online ahead of print.

Authors

Amel Laidi¹, Mohammed Ammar², Mostafa El Habib Daho³, Said Mahmoudi⁴

Affiliations

¹ LIMOSE Laboratory, Faculty of Technology, M'Hamed Bougara University, Boumerdes, Algeria.
² Engineering Systems and Telecommunication Laboratory, University M'Hamed Bougara, Boumerdes, Algeria.
³ Faculty of Technology, Biomedical Engineering Laboratory Abou Bekr Belkaid University , Tlemcen, Algeria.
⁴ Computer Science Department, Faculty of Engineering ,University of Mons. , Belgium.

PMID: 36545743
DOI: 10.2174/1573405619666221221092933

Abstract

Background: Patients with atherosclerosis have a rather high risk of showing complications, if not diagnosed quickly and efficiently.

Objective: In this paper we aim to test and compare different pre-trained deep learning models, to find the best model for atherosclerosis detection in coronary CT angiography.

Methods: We experimented with different pre-trained deep learning models and fine-tuned each model to achieve the best classification accuracy. We then used the Haar wavelet decomposition to improve the model's sensitivity.

Results: We found that the Resnet101 architecture had the best performance with an accuracy of 95.2%, 60.8% sensitivity, and 90.48% PPV. Compared to the state of the art which uses a 3D CNN and achieved 90.9% accuracy, 68.9% Sensitivity and 58.8% PPV, sensitivity was quite low. To improve the sensitivity, we chose to use the Haar wavelet decomposition and trained the CNN model with the module of the three details: Low_High, High_Low, and High_High. The best sensitivity reached 80% with the CNN_KNN classifier .

Conclusions: It is possible to perform atherosclerosis detection straight from CCTA images using a pretrained Resnet101, which has good accuracy and PPV. The low sensitivity can be improved using Haar wavelet decomposition and CNN-KNN classifier.

Keywords: Atherosclerosis; Coronary artery diseases; Deep Learning; Wavelet decomposition..