A Novel Machine Learning Approach for Detecting the Brain Abnormalities from MRI Structural Images

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7 Citations (Scopus)

Abstract

In this study, we present the investigations being pursued in our research laboratory on magnetic resonance images (MRI) of various states of brain by extracting the most significant features, and to classify them into normal and abnormal brain images. We propose a novel method based ondeep and extreme machine learning on wavelet transform to initially decompose the images, and then use various features selection and search algorithms to extract the most significant features of brain from the MRI images. By using a comparative study with different classifiers to detect the abnormality of brain images from publicly available neuro-imaging dataset, we found that a principled approach involving wavelet based feature extraction, followed by selection of most significant features using PCA technique, and the classification using deep and extreme machine learning based classifiers results in a significant improvement in accuracy and faster training and testing time as compared to previously reported studies.
Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics
EditorsTetsuo Shibuya, Hisashi Kashima, Jun Sese, Shandar Ahmad
Place of PublicationBerlin Heidellberg
PublisherSpringer
Pages94-105
Number of pages12
Volume7632
ISBN (Print)9783642341229
DOIs
Publication statusPublished - 2012
Event7thIAPR International Conference in Pattern Recognition in Bioinformatics, PRIB 2012 - Tokyo, Tokyo, Japan
Duration: 8 Nov 201210 Nov 2012

Conference

Conference7thIAPR International Conference in Pattern Recognition in Bioinformatics, PRIB 2012
Abbreviated titlePRIB 2012
CountryJapan
CityTokyo
Period8/11/1210/11/12

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Singh, L., Chetty, G., & Sharma, D. (2012). A Novel Machine Learning Approach for Detecting the Brain Abnormalities from MRI Structural Images. In T. Shibuya, H. Kashima, J. Sese, & S. Ahmad (Eds.), Pattern Recognition in Bioinformatics (Vol. 7632, pp. 94-105). Berlin Heidellberg: Springer. https://doi.org/10.1007/978-3-642-34123-6_9