Online ISSN: 2515-8260

Keywords : Stochastic gradient descent algorithm


IVUS IMAGE CATEGORIZATION USING STOCHASTIC GRADIENT DESCENT ALGORITHM

R. Charanya; J. Josphin Mary; G. Sridevi; V. Shanthi

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1431-1434
DOI: 10.31838/ejmcm.07.09.151

In IVUS, not only the lumen of the coronary arteries can be correctly shown but also the atheroma hide within the wall. In clinical research, IVUS has thus allowed progress to be made to provide a more comprehensive perspective and understanding. The cardiovascular arteries are the most common IVUS imagery target. IVUS is used to assess the quantity for atheromatous plaque in the coronary artery at any particular level. In IVUS, regression progress is a special approach for learning. The IVUS image classification using Stochastic Gradient Descent (SGD) algorithm is described in this study. The IVUS images are given to frequency domain for feature extraction and SGD algorithm is used for detection. Experimental results show the performance of proposed system.f

STOCHASTIC GRADIENT DESCENT ALGORITHM FOR GLAUCOMA DETECTION USING FREQUENCY DOMAIN FEATURES OF RETINAL IMAGES

J. Josphin Mary; R. Charanya; Konda Srinivas, M. Varaprasad Rao , B. Kavitha Rani, G. Mdhukar, K. Srujan Raju

European Journal of Molecular & Clinical Medicine, 2020, Volume 7, Issue 9, Pages 1413-1417
DOI: 10.31838/ejmcm.07.09.147

Glaucoma is a category of eye disorders that are important for good vision and damage the optic nervous. The abnormally high pressure in your eye is often responsible for its damage. Glaucoma is one of the major blindness causes for people aged 60 or over. The glaucoma detection using Stochastic Gradient Descent (SGD) algorithm is described in this study. The input retinal images are given to frequency domain for feature extraction and SGD algorithm is used for detection. Experimental results show the performance of proposed system.