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Automated detection of autism spectrum disorder using a convolutional neural network
, Article Frontiers in Neuroscience ; Volume 13 , 2020 ; Akhondzadeh, M ; Salari, S ; Zomorodi Moghadam, M ; Abdar, M ; Acharya, U. R ; Khosrowabadi, R ; Salari, V ; Sharif University of Technology
Frontiers Media S.A
2020
Abstract
Background: Convolutional neural networks (CNN) have enabled significant progress in speech recognition, image classification, automotive software engineering, and neuroscience. This impressive progress is largely due to a combination of algorithmic breakthroughs, computation resource improvements, and access to a large amount of data. Method: In this paper, we focus on the automated detection of autism spectrum disorder (ASD) using CNN with a brain imaging dataset. We detected ASD patients using most common resting-state functional magnetic resonance imaging (fMRI) data from a multi-site dataset named the Autism Brain Imaging Exchange (ABIDE). The proposed approach was able to classify ASD...
Multiclass classification of patients during different stages of Alzheimer's disease using fMRI time-series
, Article Biomedical Physics and Engineering Express ; Volume 6, Issue 5 , 2020 ; Fatemizadeh, E ; Motie Nasrabadi, A ; Sharif University of Technology
IOP Publishing Ltd
2020
Abstract
Alzheimer's Disease (AD) begins several years before the symptoms develop. It starts with Mild Cognitive Impairment (MCI) which can be separated into Early MCI and Late MCI (EMCI and LMCI). Functional connectivity analysis and classification are done among the different stages of illness with Functional Magnetic Resonance Imaging (fMRI). In this study, in addition to the four stages including healthy, EMCI, LMCI, and AD, the patients have been tracked for a year. Indeed, the classification has been done among 7 groups to analyze the functional connectivity changes in one year in different stages. After generating the functional connectivity graphs for eliminating the weak links, three...