Sanjiban Sekhar Roy1, Ching-Hsien Hsu2,3,4,*, Akash Samaran1, Ranjan Goyal1, Arindam Pande5, Valentina E. Balas6
CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 241-255, 2023, DOI:10.32604/cmes.2023.019644
Abstract Coronary artery disease (CAD) has become a significant cause of heart attack, especially among those 40 years old or younger. There is a need to develop new technologies and methods to deal with this disease. Many researchers have proposed image processing-based solutions for CAD diagnosis, but achieving highly accurate results for angiogram segmentation is still a challenge. Several different types of angiograms are adopted for CAD diagnosis. This paper proposes an approach for image segmentation using Convolution Neural Networks (CNN) for diagnosing coronary artery disease to achieve state-of-the-art results. We have collected the 2D X-ray images from the hospital, and… More >
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