Volume 20 No 20 (2022)
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An Efficient Deep Learning Algorithm for MRI Segmentation Using Kernel based CNN with M-SVM
Dr. Ayesha Banu, Santhosh Ramchander, Ramya Laxmi. K
Abstract
Image segmentation has contributed majorly on long-standing medical image processing. It used earlier considered as a minor study field in computer vision. But today the rapid evolution of deep learning and image processing in medical field using CNN model has become a major focus of study. This paper examines deep learning-based image segmentation. Initially the ideas and features of deep learning-based medical image processing are introduced. The prospective development route is widened by analyzing the three basic approaches of medical image segmentation including their precincts. A list of troublesome tissues or organs and their conventional segmentation procedures are also discussed. However, research on deep learningbased picture segmentation is still in its infancy. Medical photographs are scarce, and the data set contains only a few of these images. The generated photos are not clinically correct. These challenges are addressed by deep learning-based brain tumour segmentation. Using a database of brain tumours, this study used a kernel-based CNN with M-SVM to increase quality and minimise error rates. It is obvious that the proposed work is superior to previous work.
Keywords
M-SVM, CNN, LoG, CLAHE, Feature Extraction, SGLDM
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