Automated classification of brain images using wavelet-energy and biogeography-based optimization

Yang, Gelan; Zhang, Yudong; Yang, Jiquan; Ji, Genlin; Dong, Zhengchao; Wang, Shuihua; Feng, Chun-Mei; Wang, Qiong

doi:10.1007/s11042-015-2649-7

Cited by 156 publications

(71 citation statements)

References 33 publications

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“…Yang (2016) [13] presented a new approach respond to wavelet energy because WN shows excellent performance in practical application. The weights of SVM are optimized using BBO before classification.…”

Section: Methodsmentioning

confidence: 99%

Machine Learning Methods for Intelligent Abnormal Brain Identification

Liu

Snetkov³

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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Abstract-This survey paper describes a focused literature survey of machine learning methods in order to detect pathological brain. Based on the published time and emerging methods, this paper introduces in details the methods used in each documents. Because of the requirement to select a good approach in the process of pathological brain analysis, we compare the classification results of different methods and present a promising future.

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Section: Methodsmentioning

confidence: 99%

Machine Learning Methods for Intelligent Abnormal Brain Identification

Liu

Snetkov³

2017

Proceedings of the 2017 International Conference on Applied Mathematics, Modelling and Statistics Application (AMMSA 2017)

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“…Their accuracies were both 89.5%, which is higher than state-of-the-art methods. Future work will concentrate on the following five areas: (1) Extending our research to fruit images obtained in severe conditions, such as dried, sliced, tinned, canned, and partially covered; (2) Including additional relevant features (such as local binary patterns, wavelet-energy [41], spider-web-plot [42], wavelet packet transform, etc.) to enhance the classification performance; (3) Using interactive data mining [43], knowledge discovery [44] to test the proposed method; (4) Using compressed sensing techniques [45,46] to represent the image in sparsity domain; (5) Using advanced classification methods, like evolutionary methods inspired by Lamarch and Baldwin [29]; (6) Trying other activation functions such as ReLU.…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Wang

Zhang

et al. 2015

Entropy

Self Cite

147

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Abstract:Fruit classification is quite difficult because of the various categories and similar shapes and features of fruit. In this work, we proposed two novel machine-learning based classification methods. The developed system consists of wavelet entropy (WE), principal component analysis (PCA), feedforward neural network (FNN) trained by fitness-scaled chaotic artificial bee colony (FSCABC) and biogeography-based optimization (BBO), respectively. The K-fold stratified cross validation (SCV) was utilized for statistical analysis. The classification performance for 1653 fruit images from 18 categories showed that the proposed "WE + PCA + FSCABC-FNN" and "WE + PCA + BBO-FNN" methods achieve the same accuracy of 89.5%, higher than state-of-the-art approaches: "(CH + MP + US) + PCA + GA-FNN " of 84.8%, "(CH + MP + US) + PCA + PSO-FNN" of 87.9%, "(CH + MP + US) + PCA + ABC-FNN" of 85.4%, "(CH + MP + US) + PCA + kSVM" of 88.2%, and "(CH + MP + US) + PCA + FSCABC-FNN" of 89.1%. Besides, our methods used only 12 features, less than the number of features used by other methods. Therefore, the proposed methods are effective for fruit classification. OPEN ACCESSEntropy 2015, 17 5712

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“…Wang et al [20] proposed a combination of GA and KSVM to solve this problem. Yang et al [21] selected wavelet-energy as the features, and introduced biogeography-based optimization (BBO) to train the SVM. Wang et al [22] suggested to use stationary wavelet transform (SWT) to replace DWT, and then they proposed a hybridization of PSO and ABC (HPA) algorithm to train the classifier.…”

Section: Y Zhang Et Al / Abnormal Brain Detection By We and Qpsomentioning

confidence: 99%

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

Zhang

Yang

et al. 2016

THC

Self Cite

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Abstract. It is important to detect abnormal brains accurately and early. The wavelet-energy (WE) was a successful feature descriptor that achieved excellent performance in various applications; hence, we proposed a WE based new approach for automated abnormal detection, and reported its preliminary results in this study. The kernel support vector machine (KSVM) was used as the classifier, and quantum-behaved particle swarm optimization (QPSO) was introduced to optimize the weights of the SVM. The results based on a 5 × 5-fold cross validation showed the performance of the proposed WE + QPSO-KSVM was superior to "DWT + PCA + BP-NN", "DWT + PCA + RBF-NN", "DWT + PCA + PSO-KSVM", "WE + BPNN", "WE + KSVM", and "DWT + PCA + GA-KSVM" w.r.t. sensitivity, specificity, and accuracy. The work provides a novel means to detect abnormal brains with excellent performance.

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Automated classification of brain images using wavelet-energy and biogeography-based optimization

Cited by 156 publications

References 33 publications

Machine Learning Methods for Intelligent Abnormal Brain Identification

Machine Learning Methods for Intelligent Abnormal Brain Identification

Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization

Preliminary research on abnormal brain detection by wavelet-energy and quantum- behaved PSO

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