A 3D convolutional neural network to classify subjects as Alzheimer's disease, frontotemporal dementia or healthy controls using brain 18F-FDG PET

•Visual interpretation of [18F]-FDG-PET scans remains challenging and with the advent of new treatments, accurate diagnosis is more important than ever.•A tailor-made 3D VGG16-like network outperforms clinical interpretation by specialist physicians, achieving an overall accuracy of 89.8 % in predic...

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Bibliographic Details
Published in:	NeuroImage (Orlando, Fla.) Vol. 288; p. 120530
Main Authors:	Rogeau, Antoine, Hives, Florent, Bordier, Cécile, Lahousse, Hélène, Roca, Vincent, Lebouvier, Thibaud, Pasquier, Florence, Huglo, Damien, Semah, Franck, Lopes, Renaud
Format:	Journal Article
Language:	English
Published:	United States Elsevier Inc 01.03.2024 Elsevier Limited Elsevier
Series:	NeuroImage
Subjects:	Alzheimer Disease - diagnostic imaging Alzheimer's disease Artificial intelligence Autopsies Biomarkers Brain - diagnostic imaging Brain mapping Brain research Cerebral blood flow Classification Cognitive science Convolutional neural network Deep learning Dementia Dementia disorders FDG PET Fluorodeoxyglucose F18 Frontotemporal dementia Frontotemporal Dementia - diagnostic imaging Glucose metabolism Humans Image processing Ischemia Magnetic resonance imaging Medical imaging Metabolism Neural networks Neural Networks, Computer Neurodegeneration Neurodegenerative diseases Neuroimaging Positron emission tomography Positron-Emission Tomography - methods Retrospective Studies United States > US Deep learning Frontotemporal dementia Alzheimer's disease Convolutional neural network FDG PET
ISSN:	1053-8119, 1095-9572, 1095-9572
Online Access:	Get full text
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