Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks | Zendy

Congling Wu | Zendy; Shengwen Guo | Zendy; Yan-jia Hong | Zendy; BenHeng Xiao | Zendy; YuPeng Wu | Zendy; Qin Zhang | Zendy

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Discrimination and conversion prediction of mild cognitive impairment using convolutional neural networks

Author(s) -

Congling Wu,

Shengwen Guo,

Yan-jia Hong,

BenHeng Xiao,

YuPeng Wu,

Qin Zhang

Publication year - 2018

Publication title -

quantitative imaging in medicine and surgery

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.766

H-Index - 21

eISSN - 2223-4292

pISSN - 2223-4306

DOI - 10.21037/qims.2018.10.17

Subject(s) - convolutional neural network , cognitive impairment , computer science , cognition , artificial intelligence , machine learning , neuroscience , psychology

Recently, studies have demonstrated that machine learning techniques, particularly cutting-edge deep learning technology, have achieved significant progression on the classification of Alzheimer's disease (AD) and its prodromal phase, mild cognitive impairment (MCI). Moreover, accurate prediction of the progress and the conversion risk from MCI to probable AD has been of great importance in clinical application.

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