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

Shui-Hua Wang1,2, Yi-Ding Lv3, Yuxiu Sui3

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

Journal of Medical Systems
|November 22, 2017
PubMed
Summary

Computer vision detects alcohol use disorder (AUD) by analyzing brain structure changes. Convolutional Neural Networks (CNNs) achieved high accuracy, with stochastic pooling outperforming other methods.

Keywords:
Alcohol use disorderAverage poolingConvolutional neural networkData augmentationGraphical processing unitMax poolingStochastic pooling

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Alcohol use disorder (AUD) is a significant brain disease impacting brain structure.
  • Computer vision techniques are increasingly explored for AUD detection.

Purpose of the Study:

  • To evaluate the efficacy of Convolutional Neural Networks (CNNs) for detecting AUD using brain imaging.
  • To compare different pooling techniques within CNNs for optimal performance.

Main Methods:

  • A dataset of 235 subjects (114 alcoholic, 121 non-alcoholic) was utilized.
  • 100 images for training (with data augmentation) and 135 for testing.
  • CNN architecture including convolutional, rectified linear unit, pooling, fully connected, and softmax layers was implemented and compared with max, average, and stochastic pooling.

Main Results:

  • The proposed CNN method achieved high diagnostic performance: 96.88% sensitivity, 97.18% specificity, and 97.04% accuracy.
  • Stochastic pooling demonstrated superior performance compared to max and average pooling.
  • A CNN with five convolution layers and two fully connected layers yielded the best results.
  • GPU acceleration significantly reduced training (149x) and testing (166x) times compared to CPU.

Conclusions:

  • CNNs, particularly with stochastic pooling, show significant promise for accurate and efficient AUD detection from brain images.
  • The findings suggest a potential for AI-driven tools in diagnosing and managing alcohol use disorder.