Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Book Chapter Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Classification of myocardial ischemia in delayed contrast enhancement using machine learning Subject The topic of the resource Computer aided design (cad); Feature extraction and classification; Feed forward neural network (ffnn); Fuzzy c-means; Levenberg-marquardt back propagation (lmbp); Machine learning (ml); Magnetic resonance imaging (mri); Morphological filtering; Myocardial ischemia; Scaled conjugate gradient (scg); Segmentation Description An account of the resource This chapter addresses the classification of myocardial ischemia in delayed contrast enhancement using machine-learning techniques for magnetic resonance imaging which solves the social issue of a sudden cardiac death. To automate the classification of myocardial ischemia, the computer-aided design has a crucial path on the mixture ensemble of machine learning. The mixture ensemble of machine learning can partition a high-dimensional image in a simultaneous and competitive way. The detection and the segmentation processes are carried out through Fuzzy C-Means multispectral and single-channel algorithms along with a morphological filtering technique for feature extraction. Furthermore, the feed forward neural network (FFNN) technique is trained through the Levenberg-Marquardt Back Propagation algorithm for the classification of myocardial ischemia in delayed contrast enhancement. The proposed classification model performs well for the classification of myocardial ischemia. The rigorous process of the proposed result reveals that the FFNN classifier produces 99.9% accuracy on the classification of myocardial ischemia and also shows that the given classifier is considered one of the best methods in classifying medical myocardial ischemia. 2019 Elsevier Inc. All rights reserved. Creator An entity primarily responsible for making the resource Merjulah R.; Chandra J. Source A related resource from which the described resource is derived Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions, pp. 209-235. Publisher An entity responsible for making the resource available Elsevier Date A point or period of time associated with an event in the lifecycle of the resource 2019-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/B978-0-12-815553-0.00011-2" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/B978-0-12-815553-0.00011-2</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082611031&amp;doi=10.1016%2FB978-0-12-815553-0.00011-2&amp;partnerID=40&amp;md5=91f38c5822d9b64863cb2a1caf028adc" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85082611031&amp;doi=10.1016%2fB978-0-12-815553-0.00011-2&amp;partnerID=40&amp;md5=91f38c5822d9b64863cb2a1caf028adc</a> Rights Information about rights held in and over the resource Restricted Access Relation A related resource ISBN: 978-012815553-0 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Book chapter Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Merjulah R., Christ University, Bangalore, India; Chandra J., Christ University, Bangalore, India