Developing a Deep-Learning-Based Coronary Artery Disease Detection Technique Using Computer Tomography Images.

ABSTRACT: Coronary artery disease (CAD) is one of the major causes of fatalities across the globe. The recent developments in convolutional neural networks (CNN) allow researchers to detect CAD from computed tomography (CT) images. The CAD detection model assists physicians in identifying cardiac disease at earlier stages. The recent CAD detection models demand a high computational cost and a more significant number of images. Therefore, this study intends to develop a CNN-based CAD detection model. The researchers apply an image enhancement technique to improve the CT image quality. The authors employed You look only once (YOLO) V7 for extracting the features. Aquila optimization is used for optimizing the hyperparameters of the UNet++ model to predict CAD. The proposed feature extraction technique

SUBMITTER: Wahab Sait AR 

PROVIDER: S-EPMC10093692 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature