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Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling

  • Image & Signal Processing
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Abstract

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.

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Acknowledgments

This paper is financially supported by Natural Science Foundation of China (61602250) and Natural Science Foundation of Jiangsu Province (BK20150983).

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Authors and Affiliations

  1. Department of Informatics, University of Leicester, Leicester, LE1 7RH, UK

    Shui-Hua Wang & Yu-Dong Zhang

  2. School of Electronic Science and Engineering, Nanjing University, Nanjing, Jiangsu, 210046, China

    Shui-Hua Wang

  3. Department of Psychiatry, Nanjing Brain Hospital, Nanjing Medical University, Nanjing, Jiangsu, 210029, China

    Yi-Ding Lv & Yuxiu Sui

  4. College of Computer Science, Inner Mongolia University, Hohhot, 010012, China

    Shuai Liu

  5. Key Laboratory of Behavior Sciences, Institute of Psychology, Chinese Academy of Sciences, Beijing, 100101, China

    Su-Jing Wang

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  1. Shui-Hua Wang
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Correspondence to Shuai Liu, Su-Jing Wang or Yu-Dong Zhang.

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Wang, SH., Lv, YD., Sui, Y. et al. Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling. J Med Syst 42, 2 (2018). https://doi.org/10.1007/s10916-017-0845-x

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