Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling.
Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 1; pp. 1 - 12 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=127145183&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 127145183 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2018 vid: 42 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 127145183 127145183 127145183 10.1007/s10916-017-0845-x 127145183 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling. aug: au: Wang, Shui-Hua Lv, Yi-Ding Sui, Yuxiu Liu, Shuai Wang, Su-Jing Zhang, Yu-Dong affil: Department of Informatics, University of Leicester, LE1 7RH, Leicester, UK sug: subj: Image Processing, Computer Assisted Neural Networks (Computer) Alcoholism Diagnosis Human Female Male Middle Age Aged China Magnetic Resonance Imaging Funding Source Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Alcohol use disorder (AUD) is an important brain disease. It alters the brain structure. Recently, scholars tend to use computer vision based techniques to detect AUD. We collected 235 subjects, 114 alcoholic and 121 non-alcoholic. Among the 235 image, 100 images were used as training set, and data augmentation method was used. The rest 135 images were used as test set. Further, we chose the latest powerful technique-convolutional neural network (CNN) based on convolutional layer, rectified linear unit layer, pooling layer, fully connected layer, and softmax layer. We also compared three different pooling techniques: max pooling, average pooling, and stochastic pooling. The results showed that our method achieved a sensitivity of 96.88%, a specificity of 97.18%, and an accuracy of 97.04%. Our method was better than three state-of-the-art approaches. Besides, stochastic pooling performed better than other max pooling and average pooling. We validated CNN with five convolution layers and two fully connected layers performed the best. The GPU yielded a 149× acceleration in training and a 166× acceleration in test, compared to CPU. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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