Computer Science ›› 2017, Vol. 44 ›› Issue (Z6): 50-60.doi: 10.11896/j.issn.1002-137X.2017.6A.011

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Deep Learning for Early Diagnosis of Alzheimer’s Disease Based on Intensive AlexNet

LV Hong-meng, ZHAO Di and CHI Xue-bin   

  • Online:2017-12-01 Published:2018-12-01

Abstract: More and more people suffer from Alzheimer’s disease(AD) in China.AD is characterized by loss of memory and language ability,associated with aging.Currently,the number of Chinese patients has ranked first in the world.So,early diagnosis of AD is particularly urgent.Studies have shown that mild cognitive impairment (MCI) has a high pro-bability converted to AD.MCI may be a transition between healthy control (HC) and AD.With the advent of the era of big data,the machine learning algorithm is more and more popular in the diagnosis of disease.The method of deep lear-ning helps us classify AD,MCI and HC.The data set of magnetic resonance imaging (MRI) is from Alzheimer disease neuroimaging initiative (ADNI) as the data set.The pre-treatment of the raw brain MRI is directed by Beijing Tiantan Hospital affiliated to Capital Medical University.Images after dimensionality reduction are learned by deep convolutionalneural network (CNN) automatically.The current architecture of network is not for medical images.So,experiments focuss on improving existing networks,so as to achieve good diagnostic results.AlexNet is an excellent architecture for images classification which the experiments choose to improve.In this paper,we proposed 4 algorithms to improve the original model according to the characteristic of AD.Data ran in parallel with 8 GPUs of NVIDIA Tesla K80 of W780-G20 by Sugon.Then,we obtained 4 classifiers,AD vs.HC,AD vs.MCI,MCI vs.HC and AD vs.MCI vs.HC.Models were trained no more than 30 minutes with more than 70000 images.Finally,algorithms were evaluated by drawing ROC curve and computing sensitivity and specificity,and the better results were showed. 〖BHDWG1,WK42,WK43,WK42W〗第6A期 吕鸿蒙 ,等:基于增强AlexNet的深度学习的阿尔茨海默病的早期诊断

Key words: Alzheimer’s disease,Mild cognitive impairment,Deep learning,Convolutional neural network,Intensive Alex Net,Brain imaging,MRI

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