Abstract
Coronary Computed Tomography Angiography (CCTA) is a non-invasive imaging technique used to visualize the coronary arteries and diagnose coronary artery disease. It provides detailed 3D images which helps in identifying blockages, stenosis and plaques. However, CCTA images often suffer from noise, low contrast, and motion artifacts, which complicate accurate segmentation and analysis. The proposed work addresses the challenges in detecting and classifying coronary artery disease (CAD) from CCTA images. For accurate coronary artery segmentation, Region-Guided Mask R-CNN (RG-Mask R-CNN) combines region growing for initial region identification with Mask R-CNN for precise instance segmentation. This hybrid approach reduces the complexity of the structures in CCTA images and provides better segmentation accuracy. For feature extraction, Haralick-ResNet Fusion (HRF) captures the texture information using Haralick features and ResNet for high-level feature extraction to handle the difficulty of subtle variation differentiation in CCTA images. Finally, a Deep Convolutional Network (DeepConvNet) combines deep learning and convolutional layers to classify CAD efficiently based on the features extracted. This integrated framework offers an accurate solution (98.3%) for CAD detection from CCTA images by overcoming common issues such as noise and complex arterial structures, while improving diagnostic performance.