Alcoholism Detection by Wavelet Entropy and Support Vector Machine Trained by Genetic Algorithm

Chen, Yiyang; Yang, Jiquan; Fung, Qin-Shan; Nayeem, Arifur

doi:10.1109/roman.2018.8525823

Cited by 5 publications

(4 citation statements)

References 27 publications

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“…This proposed transfer learning approach was compared with seven state-of-the-art approaches: PAC-PSO (4), HWT (5), LR (6), CSO (7), WRE (8), SVM-GA (9), and LMCoP (10). The comparison results are shown in Table 13.…”

Section: Resultsmentioning

confidence: 99%

“…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%

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Alcoholism Identification Based on an AlexNet Transfer Learning Model

et al. 2019

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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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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alcoholism Identification Based on an AlexNet Transfer Learning Model

et al. 2019

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“…This proposed VISPNN model is compared with 10 state-of-the-art alcoholism recognition methods: PACPSO [9], LMVCoP [10], WRE [11], HWT [12], WELR [13], CSO [14], SVMGA [15], LRC [16], CNNSP [17], and ANTL [18], respectively. The comparison results are itemized in Tab.…”

Section: Comparison To Other Alcoholism Recognition Methodsmentioning

confidence: 99%

“…In their experiment, CSO was demonstrated to have better performances than four bio-inspired algorithms. Chen [15] presented a new model combining support vector machine (SVM) with genetic algorithm (GA). The combined model is abbreviated as SVMGA.…”

Section: Introductionmentioning

confidence: 99%

VISPNN: VGG-Inspired Stochastic Pooling Neural Network

Wang¹,

Khan²,

Zhang³

2022

Computers, Materials &Amp; Continua

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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

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