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

Shui-Hua Wang,Jingyuan Yang,Preetha Phillips,Junding Sun,Chenxi Huang,Chaosheng Tang,Yu-Dong Zhang

doi:10.3389/fnins.2018.00818

Abstract

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, dropout, and stochastic pooling. The output of the stochastic pooling was obtained via sampling from a multinomial distribution formed from the activations of each pooling region. In addition, we used data augmentation method to enhance the training set. In total 10 runs were implemented with the hold-out randomly set for each run.Results: The results showed that our 14-layer CNN secured a sensitivity of 98.77 ± 0.35%, a specificity of 98.76 ± 0.58%, and an accuracy of 98.77 ± 0.39%.Conclusion: Our results were compared with CNN using maximum pooling and average pooling. The comparison shows stochastic pooling gives better performance than other two pooling methods. Furthermore, we compared our proposed method with six state-of-the-art approaches, including five traditional artificial intelligence methods and one deep learning method. The comparison shows our method is superior to all other six state-of-the-art approaches.

Highlights

Multiple sclerosis is a condition that affects the brain and/or spinal cord (Chavoshi Tarzjani et al, 2018)
We proposed a novel fourteen-layer convolutional neural network with three advanced techniques: dropout, batch normalization, and stochastic pooling
(2) In order to overcome the problems happened in the traditional convolutional neural network (CNN), such as the internal co shift invariant and overfitting, we utilized batch normalization and dropout

Summary

Introduction

Multiple sclerosis (abbreviated as MS) is a condition that affects the brain and/or spinal cord (Chavoshi Tarzjani et al, 2018). It will lead to a wide range of probable symptoms, likely with balance (Shiri et al, 2018), vision, movement, sensation (Demura et al, 2016), etc. It has two main types: (i) relapsing remitting MS and (ii) primary progressive MS. More than eight out of every ten diagnosed MS patients are of the “relapsing remitting” type (Guillamó et al, 2018). MS diagnosis may be confused with other white matter diseases, such as neuromyelitis optica (NMO) (Lana-Peixoto et al, 2018), acute cerebral infarction (ACI) (Deguchi et al, 2018), acute disseminated encephalomyelitis (ADEM) (Desse et al, 2018), etc. Accurate diagnosis of MS is important for patients and following treatments. A preliminary study that identifies MS from healthy controls with the help of magnetic resonance imaging (MRI) was investigated and implemented

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