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VISPNN: VGG-Inspired Stochastic Pooling Neural Network

Shui-Hua Wang1, Muhammad Attique Khan2, Yu-Dong Zhang3,*
1 School of Mathematics and Actuarial Science, University of Leicester, Leicester, LE1 7RH, United Kingdom
2 Department of Computer Science, HITEC University Taxila, Taxila, 47080, Pakistan
3 School of Informatics, University of Leicester, Leicester, LE1 7RH, United Kingdom
* Corresponding Author: Yu-Dong Zhang. Email:
(This article belongs to this Special Issue: Recent Advances in Deep Learning for Medical Image Analysis)

Computers, Materials & Continua 2022, 70(2), 3081-3097. https://doi.org/10.32604/cmc.2022.019447

Received 14 April 2021; Accepted 28 June 2021; Issue published 27 September 2021

Abstract

Aim Alcoholism is a disease that a patient becomes dependent or addicted to alcohol. This paper aims to design a novel artificial intelligence model that can recognize alcoholism more accurately. Methods We propose the VGG-Inspired stochastic pooling neural network (VISPNN) model based on three components: (i) a VGG-inspired mainstay network, (ii) the stochastic pooling technique, which aims to outperform traditional max pooling and average pooling, and (iii) an improved 20-way data augmentation (Gaussian noise, salt-and-pepper noise, speckle noise, Poisson noise, horizontal shear, vertical shear, rotation, Gamma correction, random translation, and scaling on both raw image and its horizontally mirrored image). In addition, two networks (Net-I and Net-II) are proposed in ablation studies. Net-I is based on VISPNN by replacing stochastic pooling with ordinary max pooling. Net-II removes the 20-way data augmentation. Results The results by ten runs of 10-fold cross-validation show that our VISPNN model gains a sensitivity of 97.98 ± 1.32, a specificity of 97.80 ± 1.35, a precision of 97.78 ± 1.35, an accuracy of 97.89 ± 1.11, an F1 score of 97.87 ± 1.12, an MCC of 95.79 ± 2.22, an FMI of 97.88 ± 1.12, and an AUC of 0.9849, respectively. Conclusion The performance of our VISPNN model is better than two internal networks (Net-I and Net-II) and ten state-of-the-art alcoholism recognition methods.

Keywords

Deep learning; alcoholism; multiple-way data augmentation; VGG; convolutional neural network; stochastic pooling

Cite This Article

S. Wang, M. Attique Khan and Y. Zhang, "Vispnn: vgg-inspired stochastic pooling neural network," Computers, Materials & Continua, vol. 70, no.2, pp. 3081–3097, 2022.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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