S
Saman Sarraf
Researcher at McMaster University
Publications - 31
Citations - 1170
Saman Sarraf is an academic researcher from McMaster University. The author has contributed to research in topics: Deep learning & Convolutional neural network. The author has an hindex of 13, co-authored 29 publications receiving 848 citations. Previous affiliations of Saman Sarraf include University of Toronto.
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Journal ArticleDOI
Age differences in the functional interactions among the default, frontoparietal control, and dorsal attention networks
TL;DR: The link between age differences in inter-network connections of the FPC and DMN connectivity, and the link between FPC connectivity and performance, support the hypothesis that FC ofThe FPC influences the expression of age Differences in other networks, as well as differences in cognitive function.
Posted ContentDOI
DeepAD: Alzheimer′s Disease Classification via Deep Convolutional Neural Networks using MRI and fMRI
TL;DR: In this paper, a convolutional neural network (CNN) was used for classification of functional MRI data for the purposes of medical image analysis and Alzheimer's disease prediction, achieving a reproducible accuracy of 99.9% and 98.84% for the fMRI and MRI pipelines, respectively.
Posted Content
Classification of Alzheimer's Disease using fMRI Data and Deep Learning Convolutional Neural Networks
Saman Sarraf,Ghassem Tofighi +1 more
TL;DR: Using Convolutional Neural Network and the famous architecture LeNet-5, functional MRI data of Alzheimer's subjects from normal controls is classified and the accuracy of test data on trained data reached 96.85%.
Proceedings ArticleDOI
Deep learning-based pipeline to recognize Alzheimer's disease using fMRI data
Saman Sarraf,Ghassem Tofighi +1 more
TL;DR: This experiment suggests that the shift and scale invariant features extracted by CNN followed by deep learning classification represents the most powerful method of distinguishing clinical data from healthy data in fMRI.
Posted Content
Classification of Alzheimer's Disease Structural MRI Data by Deep Learning Convolutional Neural Networks
Saman Sarraf,Ghassem Tofighi +1 more
TL;DR: Using Convolutional Neural Network (CNN) and the famous architecture LeNet-5, structural MRI data of Alzheimer's subjects from normal controls is classified and the accuracy of test data on trained data reached 98.84%.