Heart Atherosclerosis detection based on hybrid segmentation and CMAC neural networks - et. al., G. Savitha, (Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021)
This paper illustrates how atherosclerosis pictures are segmented using a mixture of hybrid segmentation processes. The cause of coronary heart disease is atherosclerosis (CHD). Due to its benefits, such as the ability to create a whole division of the colour image and to prevent its segmentation over traditional algorithms, the proposed marker hybrid algorithm that combines random walk segmentation with particle swarm optimization for medical image segmentation and analysis is very interesting. In paper, the area of interest (ROI) image values with proposed coronary atherosclerosis technique are established. The new hybrid algorithm suggested guarantees high segmentation precision with optimisation.