GS11-12 Classification of Hippocampal Region using Extreme Learning Machine

Title:	GS11-12 Classification of Hippocampal Region using Extreme Learning Machine
Publication:	ICAROB2017
Volume:	22
Pages:	735-742
ISSN:	2188-7829
DOI:	10.5954/ICAROB.2017.GS11-12
Author(s):	Muhammad Hafiz Md Zaini, Mohd Ibrahim Shapiai, Ahmad Rithauddin Mohamed, Norrima Mokhtar, Zuwairie Ibrahim
Publication Date:	January 19, 2017
Keywords:	Extreme learning machine, Hippocampal segmentation, Magnetic resonance imaging, Neuroimaging
Abstract:	Important brain parts like hippocampal usually being manually segmented by doctors. But with the introduction of hybrid between machine learning along with neuroimaging technique, it has proved to shows some promising results regarding on segmenting subcortical structures. However, it is known that Extreme Learning Machine (ELM) is to be superior machine learning technique. This study will investigate on the usage of ELM to segment hippocampal by using various hidden nodes configuration. This study also will address on the usage of full image and region of interest (ROI) using ELM. Bag of features is used as a feature extractor where it will segment the hippocampal of the MRI in order to get its visual words. ELM will used it to learn its feature. Results shows that with suitable hidden nodes, it could achieve up to 100% performance on both cases for full image and ROI in hippocampal segmentation.
PDF File:	https://alife-robotics.co.jp/members2017/icarob/data/html/data/GS_pdf/GS11/GS11-12.pdf
Copyright:	© The authors. This article is distributed under the terms of the Creative Commons Attribution License 4.0, which permits non-commercial use, distribution and reproduction in any medium, provided the original work is properly cited. See for details: https://creativecommons.org/licenses/by-nc/4.0/

HOME