Alcoholism via 6-layer customized deep convolution neural network

  • Ziquan Zhu Department of Civil Engineering, University of Florida, Gainesville, United States
Keywords: Deep convolution neural network, CT image, Alcoholism


Background: Alcoholism is caused by excessive alcohol into the human body. Alcohol primarily damages the central nervous system of the human body and causes the nervous system function disorder and inhibition. Severe addiction can lead to respiratory circulation center inhibition, paralysis and even death. So far, the diagnosis of alcoholism is done by radiologist's manual CT examination. However, the diagnosis process is time-consuming, subjective and boring for doctors. External factors, such as extreme fatigue, lack of sleep and mental concentration, can easily affect the diagnosis process.
Methods: In order to solve this problem, this paper proposed a new neural network based on computer vision, which used deep convolution neural network to diagnose alcoholism automatically. A total of 216 brain images were collected. In the 6-layer customized deep convolution neural network structure, there were four convolution layers and two fully connected layers, and each convolution layer was connected with a pooling layer.
Results: The results showed that the accuracy, sensitivity, specificity, precision, F1, MCC and FMI were 95.96%±1.44%, 95.96%±1.66%, 95.95%±1.67%, 95.73%±1.72%, 95.84%±1.48%, 91.92%±2.87% and 95.84%±1.48% respectively.
Conclusion: It can be concluded from comparison results that the proposed neural network structure is more effective than four state-of-the-art approaches. The proposed method has high accuracy and can be used as a diagnostic method for alcoholism.


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. Kumar S, Ghosh S, Sinha RK. Using computational classifiers to detect chronic alcoholism. J Clin Eng. 2016;41(2):90-4.

Rodrigues JdC, Filho PPR, Peixoto E, N AK, de Albuquerque VHC. Classification of EEG signals to detect alcoholism using machine learning techniques. Pattern Recognit Lett. 2019;125:140-9.

Anuragi A, Sisodia DS. Empirical wavelet transform based automated alcoholism detecting using EEG signal features. Biomed Signal Process Control. 2020;57.

Hou X-X. Alcoholism detection by medical robots based on Hu moment invariants and predator-prey adaptive-inertia chaotic particle swarm optimization. Comput Electr Eng. 2017;63:126-38.

Han L. Identification of Alcoholism based on wavelet Renyi entropy and three-segment encoded Jaya algorithm. Complexity. 2018;2018.

Qian P. Cat Swarm Optimization applied to alcohol use disorder identification. Multimed Tools Appl. 2018;77(17):22875-96.

Chen X. Alcoholism detection by wavelet eergy entropy and linear regression classifier. Comput Model Eng Sci. 2021;127:325-43.

Barik A, Rai RK, Chowdhury A. Alcohol use-related problems among a rural indian population of west bengal: an application of the alcohol use disorders identification test (AUDIT). Alcohol Alcohol. 2016;51(2):215-23.

Wang S-H, Muhammad K, Lv Y, Sui Y, Han L, Zhang Y-D. Identification of alcoholism based on wavelet Renyi entropy and three-segment encoded jaya algorithm. Complexity. 2018;2018:1-13.

Woolrich MW, Jbabdi S, Patenaude B, Chappell M, Makni S, Behrens T, et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage. 2009;45(1 Suppl):S173-86.

Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, Johansen-Berg H, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208-19.

Albawi S, Mohammed TA, Al-Zawi S, editors. Understanding of a convolutional neural network. International Conference on Engineering and Technology; 2017; Akdeniz Univ, Antalya, TURKEY: IEEE.

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

Tang C. Twelve-layer deep convolutional neural network with stochastic pooling for tea category classification on GPU platform. Multimed Tools Appl. 2018;77(17):22821-39.

Ahmed HOA, Nandi AK. Connected components-based colour image representations of vibrations for a two-stage fault diagnosis of roller bearings using convolutional neural networks. Chin J Mech Eng. 2021;34(1):21.

Pan C. Abnormal breast identification by nine-layer convolutional neural network with parametric rectified linear unit and rank-based stochastic pooling. J Comput Sci. 2018;27:57-68.

Pan C. Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU.J Comput Sci. 2018;28:1-10.

Nguyen H, Tran T. Three-dimensional shape reconstruction from single-shot speckle image using deep convolutional neural networks. Opt Lasers Eng. 2021;143:10.

Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84-90.

Zhao G. Polarimetric synthetic aperture radar image segmentation by convolutional neural network using graphical processing units. J Real Time Image Process. 2018;15(3):631-42.

Huang C. Multiple sclerosis identification by 14-layer convolutional neural network with batch normalization, dropout, and stochastic pooling. Front Neurosci. 2018;12.

Mariani S, Rendu Q, Urbani M, Sbarufatti C. Causal dilated convolutional neural networks for automatic inspection of ultrasonic signals in non-destructive evaluation and structural health monitoring. Mech Syst Signal Proc. 2021;157:22.

Muhammad K. Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation. Multimed Tools Appl. 2019;78(3):3613-32.

Xie S. Alcoholism identification based on an AlexNet transfer learning model. Front Psychiatry. 2019;10.

Rogers MSJ, Bithell M, Brooks SM, Spencer T. VEdge_Detector: automated coastal vegetation edge detection using a convolutional neural network. Int J Remote Sens. 2021;42(13):4809-39.

Deshpande P, Belwalkar A, Dikshit O, Tripathi S. Historical land cover classification from CORONA imagery using convolutional neural networks and geometric moments. Int J Remote Sens. 2021;42(13):5148-75.

Wang S-H. DenseNet-201-based deep neural network with composite learning factor and precomputation for multiple sclerosis classification. ACM Trans Multimedia Comput Commun Appl. 2020;16(2s):Article 60.

Sangaiah AK. Alcoholism identification via convolutional neural network based on parametric ReLU, dropout, and batch normalization. Neural Comput Appl. 2020;32:665-80.

Dalal S, Khalaf OI. Prediction of occupation stress by implementing convolutional neural network techniques. J Cases Inf Technol. 2021;23(3):27-42.

Zhang Y-D, Dong Z-C. Advances in multimodal data fusion in neuroimaging: Overview, challenges, and novel orientation. Inf Fusion. 2020;64:149-87.

Wang S-H. Covid-19 Classification by FGCNet with deep feature fusion from graph convolutional network and convolutional neural network. Inf Fusion. 2021;67:208-29.

Peker M. Classification of hyperspectral imagery using a fully complex-valued wavelet neural network with deep convolutional features. Expert Syst Appl. 2021;173:11.

Satapathy SC. A five-layer deep convolutional neural network with stochastic pooling for chest CT-based COVID-19 diagnosis. Mach Vis Appl. 2021;32.

How to Cite
ZhuZ. (2021). Alcoholism via 6-layer customized deep convolution neural network. STEMedicine, 2(7), e93.
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