Understanding the Brain via fMRI Classification

Lavneet Singh, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookChapter

2 Citations (Scopus)

Abstract

In this chapter, we present investigations on magnetic resonance imaging (MRI) of various states of brain by extracting the most significant features in order to classify brain images into normal and abnormal. We describe a novel method based on the wavelet transform to initially decompose the images, followed by the use of various feature selection algorithms to extract the most significant brain features from the MRI images. This chapter demonstrates the use of different classifiers to detect abnormal brain images from a publicly available neuroimaging dataset. A wavelet-based feature extraction followed by selection of the most significant features using principal component analysis (PCA)/quadratic discriminant analysis (QDA) with classification using learning-based classifiers results in a significant improvement in accuracy as compared with previously reported studies and to better understanding of brain abnormalities.
Original languageEnglish
Title of host publicationSpringer Handbook of Bio-/Neuro-Informatics
EditorsNikola Kasabov
Place of PublicationBerlin, Germany
PublisherSpringer
Chapter40
Pages703-711
Number of pages9
Edition1
ISBN (Electronic)9783642305740
ISBN (Print)9783642305733
DOIs
Publication statusPublished - 2014

Publication series

NameSpringer Handbooks
PublisherSpringer
ISSN (Print)2522-8692
ISSN (Electronic)2522-8706

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Singh, L., & CHETTY, G. (2014). Understanding the Brain via fMRI Classification. In N. Kasabov (Ed.), Springer Handbook of Bio-/Neuro-Informatics (1 ed., pp. 703-711). (Springer Handbooks). Berlin, Germany: Springer. https://doi.org/10.1007/978-3-642-30574-0_40