It has the ability to extract features along multiple scales and arbitrary directions.
Published in Chapter:
Automated Detection of Brain Abnormalities Using Multi-Directional Features and Randomized Learning: A Comparative Study
Deepak Ranjan Nayak (Indian Institute of Information Technology, Design, and Manufacturing, Kancheepuram, India), Dibyasundar Das (National Institute of Technology, Rourkela, India), Ratnakar Dash (National Institute of Technology, Rourkela, India), and Banshidhar Majhi (Indian Institute of Information Technology, Design, and Manufacturing, Kancheepuram, India)
Copyright: © 2020
|Pages: 22
DOI: 10.4018/978-1-7998-2120-5.ch002
Abstract
Automated detection of brain abnormalities through magnetic resonance imaging (MRI) has made a significant stride in the past decade. The feature extractors exploited in the literature suffer from issues like limited directional selectivity and high dimensionality, and the classifiers used have critical drawbacks like slow learning speed, poor computational scalability, and trivial human intervention. The fast curvelet transform (FCT) and ripplet-II transform (R2T) provides improved discriminant ability and high directional selectivity. Extreme learning machine (ELM), a randomized learning algorithm for single layer feed-forward neural network, has received significant attention as it provides good generalization performance at much faster speed. In this chapter, the authors compare the effectiveness of two feature extractors based on FCT and R2T along with different ELM algorithms. These schemes have been evaluated on three brain MR datasets and comparative analyses have been made on several combinations of methods. Finally, the potential of the best scheme is compared to the state of the art.