Title:Deep Learning Models for Coronary Atherosclerosis Detection in Coronary CT
Angiography
Volume: 20
Author(s): Amel Laidi*, Mohammed Ammar*, Mostafa El Habib Daho and Said Mahmoudi
Affiliation:
- LIMOSE Laboratory, Faculty of Technology, M’Hamed Bougara University, Boumerdes, Algeria
- Engineering Systems and Telecommunication Laboratory, University M’Hamed Bougara, Boumerdes, Algeria
Keywords:
Deep learning, Atherosclerosis, Coronary artery diseases, Wavelet decomposition, Angiography, Resnet101.
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.
Conclusion:
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.
