Open AccessDiscrimination and conversion prediction of mild cognitive impairment using convolutional neural networks
Author(s) -
Congling Wu,
Sen 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 , transfer of learning , cognitive impairment , neuroimaging , artificial intelligence , deep learning , alzheimer's disease neuroimaging initiative , computer science , pattern recognition (psychology) , cognition , medicine , disease , psychiatry
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.