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Classification of Alzheimer disease among susceptible brain regions
Author(s) -
Ahmad Fayyaz,
Zulifqar Hifza,
Malik Tamoor
Publication year - 2019
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/ima.22308
Subject(s) - entorhinal cortex , support vector machine , linear discriminant analysis , receiver operating characteristic , context (archaeology) , neuroimaging , posterior cingulate , artificial intelligence , alzheimer's disease , pattern recognition (psychology) , logistic regression , neuroscience , computer science , functional magnetic resonance imaging , medicine , psychology , hippocampus , disease , machine learning , pathology , biology , paleontology
Statistical and machine learning techniques are frequently employed in the study of neuroimaging data for finding Alzheimer disease (AD) in clinical studies and in additional inquiries about research settings. AD affects the whole brain and as a result the quality of life, where most affected regions are the hippocampus (HP), middle temporal gyrus (MTG), entorhinal cortex, and posterior cingulate cortex (PCC). We used well‐known classification methods to diagnose the affected regions of the brain at different stages of age using biomarker modalities and functional magnetic resonance imaging (fMRI) at the resting state, and later marked the affected brain region on MRI. We have used well‐known support vector machine (SVM), Fisher's linear discriminant analysis, artificial neural network, and logistic regression for the classification of AD. In the context of receiver operating characteristic (ROC) curves, an SVM provided the best classification among AD stages. Moreover, analysis showed development of AD.