Classification of myocardial ischemia in delayed contrast enhancement using machine learning

Title

Classification of myocardial ischemia in delayed contrast enhancement using machine learning

Creator

Merjulah R.; Chandra J.

Description

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.

Source

Intelligent Data Analysis for Biomedical Applications: Challenges and Solutions, pp. 209-235.

Date

2019-01-01

Publisher

Elsevier

Subject

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

Coverage

Merjulah R., Christ University, Bangalore, India; Chandra J., Christ University, Bangalore, India

Rights

Restricted Access

Relation

ISBN: 978-012815553-0

Format

Online

Language

English

Type

Book chapter

Identifier

https://doi.org/10.1016/B978-0-12-815553-0.00011-2

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