Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm

Wang, Shuihua; Muhammad, Khan; Lv, Yiding; Sui, Yuxiu; Han, Liangxiu; Zhang, Yudong

doi:10.1155/2018/3198184

Cited by 30 publications

(16 citation statements)

References 34 publications

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“…In recent years, a convolution neural network (CNN) has had excellent performance in image and video recognition, [22][23][24] recommender system, 25,26 and nature language processing. 27,28 Compared to the traditional machine learning methods, which learn the hand-crafted features, [29][30][31] CNN is independent from prior knowledge and human effort in feature design.…”

Section: Methodsmentioning

confidence: 99%

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Wang

Sun

Mehmood

et al. 2019

Concurrency and Computation

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Cerebral micro-bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six-convolution layer and three fully-connected layer CNN, nine-layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state-of-the-art methods. KEYWORDScerebral micro-bleeding, convolution neural network, detection, intra-cerebral hemorrhage, stochastic pooling, stroke INTRODUCTIONCerebral micro-bleedings (CMBs) are small chronic brain hemorrhages, which can be caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke and memory clinics that is initially set up for research purposes. 1 CMBs indicate hemorrhage-prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra-cerebral hemorrhage (ICH), dementia, and death. 2 Previous studies show that, in a range of 18% to 68%, patients with ischemic stroke/transient ischemic attack have a high prevalence of CMBs. There are 23% of prevalence in patients with first-ever ischemic stroke and 44% in patients with recurrent ischemic stroke reported by a previous systematic review. 3 The strong relationship with CMBs and increased risk of ICH suggests that the antithrombotic drugs Concurrency Computat Pract Exper. 2020;32:e5130. wileyonlinelibrary.com/journal/cpe to test the performance of the proposed structure of CNN based on the stochastic pooling, we compared different pooling methods, including average pooling, max pooling, and stochastic pooling. Meanwhile, we compared CNN with different convolution number and the state-of-the-art method for CMB detection. The experiment result shows that our designed structure of CNN based on the stochastic pooling achieved the best performance in sensitivity as 97.22%, specificity as 97.35%, accuracy as 97.28%, and precision as 97.35%.The limitation of our work is that we may need to move the window from top to bottom and from left to right. This is time-consuming. It is better to use concurrent convolution in the future studies, and we may need to replace the fully connected layers with conv layers. Another limitation is that we have two labels (CMB and ...

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Section: Methodsmentioning

confidence: 99%

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Wang

Sun

Mehmood

et al. 2019

Concurrency and Computation

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“…Least square method (PLS) and support vector machine (BP-ANN and SVM) were used for linear and non-linear quantitative model 19 , 38 – 41 , respectively.…”

Section: Methodsmentioning

confidence: 99%

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Liang

Dong

et al. 2018

Sci Rep

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Withering is the first step in the processing of congou black tea. With respect to the deficiency of traditional water content detection methods, a machine vision based NDT (Non Destructive Testing) method was established to detect the moisture content of withered leaves. First, according to the time sequences using computer visual system collected visible light images of tea leaf surfaces, and color and texture characteristics are extracted through the spatial changes of colors. Then quantitative prediction models for moisture content detection of withered tea leaves was established through linear PLS (Partial Least Squares) and non-linear SVM (Support Vector Machine). The results showed correlation coefficients higher than 0.8 between the water contents and green component mean value (G), lightness component mean value (L*) and uniformity (U), which means that the extracted characteristics have great potential to predict the water contents. The performance parameters as correlation coefficient of prediction set (Rp), root-mean-square error of prediction (RMSEP), and relative standard deviation (RPD) of the SVM prediction model are 0.9314, 0.0411 and 1.8004, respectively. The non-linear modeling method can better describe the quantitative analytical relations between the image and water content. With superior generalization and robustness, the method would provide a new train of thought and theoretical basis for the online water content monitoring technology of automated production of black tea.

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“…Qian (7) employed the cat swarm optimization (CSO) and obtained excellent results in the diagnosis of alcoholism. Han (8) used wavelet Renyi entropy (WRE) to generate a new biomarker; whereas Chen (9) used a support vector machine, which was trained using a genetic algorithm (SVM-GA) approach. Jenitta and Ravindran (10) proposed a local mesh vector co-occurrence pattern (LMCoP) feature for assisting diagnosis.…”

Section: Introductionmentioning

confidence: 99%

Alcoholism Identification Based on an AlexNet Transfer Learning Model

et al. 2019

Self Cite

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Aim: This paper proposes a novel alcoholism identification approach that can assist radiologists in patient diagnosis. Method: AlexNet was used as the basic transfer learning model. The global learning rate was small, at 10 −4 , and the iteration epoch number was at 10. The learning rate factor of replaced layers was 10 times larger than that of the transferred layers. We tested five different replacement configurations of transfer learning. Results: The experiment shows that the best performance was achieved by replacing the final fully connected layer. Our method yielded a sensitivity of 97.44%± 1.15%, a specificity of 97.41 ± 1.51%, a precision of 97.34 ± 1.49%, an accuracy of 97.42 ± 0.95%, and an F1 score of 97.37 ± 0.97% on the test set. Conclusion: This method can assist radiologists in their routine alcoholism screening of brain magnetic resonance images.

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Identification of Alcoholism Based on Wavelet Renyi Entropy and Three-Segment Encoded Jaya Algorithm

Cited by 30 publications

References 34 publications

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Cerebral micro‐bleeding identification based on a nine‐layer convolutional neural network with stochastic pooling

Prediction of Moisture Content for Congou Black Tea Withering Leaves Using Image Features and Nonlinear Method

Alcoholism Identification Based on an AlexNet Transfer Learning Model

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