Atherosclerosis; Coronary artery diseases; Deep Learning; Wavelet decomposition.; Radiology, Nuclear Medicine and imaging
Abstract :
[en] [en] 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.
Ammar, Mohammed; Engineering Systems and Telecommunication Laboratory, University M'Hamed Bougara, Boumerdes, Algeria
Daho, Mostafa El Habib; Faculty of Technology, Biomedical Engineering Laboratory Abou Bekr Belkaid University , Tlemcen, Algeria
Mahmoudi, Saïd ; Université de Mons - UMONS > Faculté Polytechnique > Service Informatique, Logiciel et Intelligence artificielle
Language :
English
Title :
Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT Angiography.
Publication date :
21 December 2022
Journal title :
Current Medical Imaging Reviews
ISSN :
1573-4056
Publisher :
Bentham Science Publishers Ltd., United Arab Emirates
Volume :
19
Peer reviewed :
Peer Reviewed verified by ORBi
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
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