Studies on classification of FMRI data using deep learning approach

Mohd Suhaimi, Nur Farahana and Htike@Muhammad Yusof, Zaw Zaw and Alang Md Rashid, Nahrul Khair (2015) Studies on classification of FMRI data using deep learning approach. In: International Postgraduate Conference on Engineering Research (IPCER) 2015 , 27-28 Oct 2015, Gombak. (In Press)

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Abstract

Brain as main server for entire human body is a complex composition. It is a challenging task to read and interpret the brain. Functional magnetic resonance imaging (fMRI) has become one of the means to do the task. fMRI is a non-invasive technique to measure brain activity of a human subject according to various stimuli. However, the fMRI datasets for each subject is huge and high-dimensional. For instance, the dataset has four dimensions for 3D images time series. Pre-processing and analysing using pattern recognition are insignificance for datasets with varied anatomical structures and dimensions. On the other hand, supervised learning or biomarker is employed to reduce the curse-of-dimensionality of fMRI datasets. Yet, the process is difficult and subjective to the labeled datasets. Therefore, a well-versed approach in signal processing, natural language processing (NLP) and object recognition, known as deep learning is seen to have higher standard than usual classification approach. Deep learning is the improved version of neural network with higher capability and accuracy. This paper aims to review the deep learning approach in fMRI classifications based on three studies on fMRI data classification.

Item Type:	Conference or Workshop Item (Full Paper)
Additional Information:	6919/48011
Uncontrolled Keywords:	FMRI, Deep Learning, Classification and Neural Networ
Subjects:	T Technology > T Technology (General)
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Engineering
Depositing User:	Mr. Zaw Zaw Htike
Date Deposited:	22 Jan 2016 15:19
Last Modified:	22 Jan 2016 15:20
URI:	http://irep.iium.edu.my/id/eprint/48011

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