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RETRACTED ARTICLE: Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization

  • Intelligent Biomedical Data Analysis and Processing
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This article was retracted on 24 April 2024

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Abstract

Alcoholism changes the structure of brain. Several somatic marker hypothesis network-related regions are known to be damaged in chronic alcoholism. Neuroimaging approach can help us better understanding the impairment discovered in alcohol-dependent subjects. In this research, we recruited subjects from participating hospitals. In total, 188 abstinent long-term chronic alcoholic participants (95 men and 93 women) and 191 non-alcoholic control participants (95 men and 96 women) were enrolled in our experiment via computerized diagnostic interview schedule version IV and medical history interview employed to determine whether the applicants can be enrolled or excluded. The Siemens Verio Tim 3.0 T MR scanner (Siemens Medical Solutions, Erlangen, Germany) was employed to scan the subjects. Then, we proposed a 10-layer convolutional neural network for the diagnosis based on imaging, including three advanced techniques: parametric rectified linear unit (PReLU); batch normalization; and dropout. The structure of network is fine-tuned. The results show that our method secured a sensitivity of 97.73 ± 1.04%, a specificity of 97.69 ± 0.87%, and an accuracy of 97.71 ± 0.68%. We observed the PReLU gives better performance than ordinary ReLU, clipped ReLU, and leaky ReLU. The batch normalization and dropout gained enhanced performance as batch normalization overcame the internal covariate shift and dropout got over the overfitting. The results of our proposed 10-layer CNN model show its performance better than seven state-of-the-art approaches.

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References

  1. Haeny AM et al (2018) Method effects of the relation between family history of alcoholism and parent reports of offspring impulsive behavior. Addict Behav 87:251–259

    Article  Google Scholar 

  2. Wlodarczyk O et al (2017) Protective mental health factors in children of parents with alcohol and drug use disorders: a systematic review. PLoS ONE 12(6):e0179140

    Article  Google Scholar 

  3. Stahre M (2014) Contribution of excessive alcohol consumption to deaths and years of potential life lost in the United States. Prev Chronic Dis 11:E109

    Article  Google Scholar 

  4. Coleman LG et al (2018) HMGB1/IL-1 beta complexes regulate neuroimmune responses in alcoholism. Brain Behav Immun 72:61–77

    Article  Google Scholar 

  5. Ho AL et al (2018) The nucleus accumbens and alcoholism: a target for deep brain stimulation. Neurosurg Focus 45(2):10. Article ID. E12

  6. Pereira CR et al (2018) A survey on computer-assisted Parkinson’s disease diagnosis. Artif Intell Med. https://doi.org/10.1016/j.artmed.2018.08.007

    Article  Google Scholar 

  7. Pinheiro PR et al (2018) Evaluation of the Alzheimer’s disease clinical stages under the optics of hybrid approaches in Verbal Decision Analysis. Telemat Inform 35(4):776–789

    Article  Google Scholar 

  8. Cheng H (2018) Classification of Alzheimer’s disease based on eight-layer convolutional neural network with leaky rectified linear unit and max pooling. J Med Syst 42:Article ID. 42

  9. Hou X-X (2018) Seven-layer deep neural network based on sparse autoencoder for voxelwise detection of cerebral microbleed. Multimed Tools Appl 77(9):10521–10538

    Article  Google Scholar 

  10. Alweshah M et al (2015) Hybridizing firefly algorithms with a probabilistic neural network for solving classification problems. Appl Soft Comput 35:513–524

    Article  Google Scholar 

  11. Hou X-X (2017) Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization. Comput Electr Eng 63:126–138

    Article  Google Scholar 

  12. Sharma M et al (2018) Dual-tree complex wavelet transform-based features for automated alcoholism identification. Int J Fuzzy Syst 20(4):1297–1308

    Article  Google Scholar 

  13. Han L (2018) Identification of alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm. Complexity 2018:Article ID. 3198184

  14. Lima D (2018) Alcoholism detection in magnetic resonance imaging by Haar wavelet transform and back propagation neural network. In: AIP conference proceedings 1955(1):Article ID. 040012

  15. Qian P (2018) Cat swarm optimization applied to alcohol use disorder identification. Multimed Tools Appl 77(17):22875–22896

    Article  Google Scholar 

  16. Lv Y-D (2018) Alcoholism detection by data augmentation and convolutional neural network with stochastic pooling. J Med Syst 42(1):Article ID. 2

  17. Pereira DR et al (2018) Stroke lesion detection using convolutional neural networks. In: 2018 International joint conference on neural networks (IJCNN), pp 1–6

  18. Velasco JM et al (2018) Combining data augmentation, EDAs and grammatical evolution for blood glucose forecasting. Memet Comput 10(3):267–277

    Article  Google Scholar 

  19. Li S et al (2018) A novel recurrent neural network for manipulator control with improved noise tolerance. IEEE Trans Neural Netw Learn Syst 29(5):1908–1918

    Article  MathSciNet  Google Scholar 

  20. Ferreira MVD et al (2018) Convolutional neural network and texture descriptor-based automatic detection and diagnosis of glaucoma. Expert Syst Appl 110:250–263

    Article  Google Scholar 

  21. Chaiyasarn K et al (2018) Crack detection in historical structures based on convolutional neural network. Int J Geomate 15(51):240–251

    Article  Google Scholar 

  22. Nair V et al (2010) Rectified linear units improve restricted boltzmann machines. In: 27th international conference on machine learning (ICML). Haifa, Israel: ACM, pp 807–814

  23. Pagola M et al (2017) A study on the cardinality of ordered average pooling in visual recognition. Pattern Recognit Image Anal 10255:437–444

    Article  MathSciNet  Google Scholar 

  24. Ahmed N (2018) Data-free/data-sparse softmax parameter estimation with structured class geometries. IEEE Signal Process Lett 25(9):1408–1412

    Article  Google Scholar 

  25. Murugadoss R et al (2014) Universal approximation of nonlinear system predictions in sigmoid activation functions using artificial neural networks. In: Krishnan N, Karthikeyan M (eds) International conference on computational intelligence and computing research. IEEE, Coimbatore, INDIA, pp 1062–1067

  26. Wan G et al (2009) Shortwave memory power amplifier linearization based on Tanh neural network predistorter. In: International conference on innovation management. IEEE, Wuhan, China, pp 63–66

  27. Barushka A et al (2016) Spam filtering using regularized neural networks with rectified linear units. In: 15th international conference of the Italian association for artificial intelligence (AIIA). Springer, Genova, Italy, pp 65–75

  28. Hannun A et al (2014) Deep speech: scaling up end-to-end speech recognition. arXiv preprint: Article ID. 1412.5567

  29. Maas AL et al (2013) Rectifier nonlinearities improve neural network acoustic models. ICML 30(1):3

    Google Scholar 

  30. He KM et al (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: IEEE international conference on computer vision (ICCV). IEEE, Santiago, Chile, pp 1026–1034

  31. Srivastava N et al (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MathSciNet  Google Scholar 

  32. Li S et al (2017) Distributed winner-take-all in dynamic networks. IEEE Trans Autom Control 62(2):577–589

    Article  MathSciNet  Google Scholar 

  33. Li S et al (2018) Discrete-time consensus filters for average tracking of time-varying inputs on directed switching graphs. Asian J Control 20(2):919–934

    Article  MathSciNet  Google Scholar 

  34. Ioffe S et al (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the 32nd international conference on international conference on machine learning (ICML). ACM, pp 448–456

  35. Zhang Z et al (2016) A virtual laboratory system with biometric authentication and remote proctoring based on facial recognition. Comput Educ J 7(4):74–84

    Google Scholar 

  36. Zhang Z et al (2016) A virtual laboratory combined with biometric authentication and 3D reconstruction. In: ASME 2016 international mechanical engineering congress and exposition. ASME, Phoenix, Arizona, USA, p V005T06A049

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Acknowledgements

This study was financially supported by Natural Science Foundation of China (61602250), Open fund for Jiangsu Key Laboratory of Advanced Manufacturing Technology (HGAMTL-1703), Key Laboratory of Measurement and Control of Complex Systems of Engineering, Southeast University, Ministry of Education (MCCSE2017A02), Open Fund of Guangxi Key Laboratory of Manufacturing System and Advanced Manufacturing Technology (17-259-05-011K), Henan Key Research and Development Project (182102310629).

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Authors and Affiliations

  1. School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, 454000, Henan, People’s Republic of China

    Shui-Hua Wang & Yu-Dong Zhang

  2. School of Architecture Building and Civil Engineering, Loughborough University, Loughborough, LE11 3TU, UK

    Shui-Hua Wang

  3. Digital Contents Research Institute, Sejong University, Seoul, Republic of Korea

    Khan Muhammad

  4. School of Earth Sciences and Engineering, Sun Yat⁃Sen University, Guangzhou, 510275, China

    Jin Hong

  5. School of Computing Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, 632014, India

    Arun Kumar Sangaiah

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  1. Shui-Hua Wang
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  2. Khan Muhammad
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  5. Yu-Dong Zhang
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Correspondence to Yu-Dong Zhang.

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This article has been retracted. Please see the retraction notice for more detail: https://doi.org/10.1007/s00521-024-09873-x"

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Wang, SH., Muhammad, K., Hong, J. et al. RETRACTED ARTICLE: Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Comput & Applic 32, 665–680 (2020). https://doi.org/10.1007/s00521-018-3924-0

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  • DOI: https://doi.org/10.1007/s00521-018-3924-0

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