Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling

Aim: Multiple sclerosis is a severe brain and/or spinal cord disease. It may lead to a wide range of symptoms. Hence, the early diagnosis and treatment is quite important.Method: This study proposed a 14-layer convolutional neural network, combined with three advanced techniques: batch normalization...

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Bibliographic Details
Main Authors: Shui-Hua Wang, Chaosheng Tang, Junding Sun, Jingyuan Yang, Chenxi Huang, Preetha Phillips, Yu-Dong Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2018-11-01
Series:Frontiers in Neuroscience
Subjects:
multiple sclerosis
deep learning
convolutional neural network
batch normalization
dropout
stochastic pooling
Online Access:https://www.frontiersin.org/article/10.3389/fnins.2018.00818/full

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https://www.frontiersin.org/article/10.3389/fnins.2018.00818/full

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