Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging

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ISSN Print: 1937-6871
ISSN Online: 1937-688X
www.scirp.org/journal/jbise
E-mail: jbise@scirp.org

"Computer-aided differential diagnosis system for Alzheimer’s disease based on machine learning with functional and morphological image features in magnetic resonance imaging"
written by Yasuo Yamashita, Hidetaka Arimura, Takashi Yoshiura, Chiaki Tokunaga, Ohara Tomoyuki, Koji Kobayashi, Yasuhiko Nakamura, Nobuyoshi Ohya, Hiroshi Honda, Fukai Toyofuku,
published by Journal of Biomedical Science and Engineering, Vol.6 No.11, 2013

has been cited by the following article(s):

[1]	Neuroimaging computer‐aided diagnosis systems for Alzheimer's disease
	International Journal of Imaging Systems and Technology, 2019

[2]	Comparison of the WEKA and SVM-light based on support vector machine in classifying Alzheimer's disease using structural features from brain MR imaging
	2019

[3]	Automated 3D labelling of fibroblasts and endothelial cells in SEM-imaged placenta using deep learning
	2019

[4]	Interprofessional Collaborative System to Raise Awareness and Understanding of Dementia Using an Action Observation Method
	2016 AAAI Spring Symposium Series, 2016

[5]	Applying Machine Learning Techniques in Diagnosing Bacterial Vaginosis
	ProQuest Dissertations Publishing, 2014

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[1]	Comparison of the WEKA and SVM-light based on support vector machine in classifying Alzheimer’s disease using structural features from brain MR imaging Journal of Physics: Conference Series, 2019 DOI:10.1088/1742-6596/1248/1/012003

[2]	Neuroimaging computer-aided diagnosis systems for Alzheimer's disease International Journal of Imaging Systems and Technology, 2018 DOI:10.1002/ima.22300