Weights Optimization of Neural Network via Improved Bco Approach

Zhang, Yudong; Wu, Lenan

doi:10.2528/pier08051403

Cited by 71 publications

(20 citation statements)

References 23 publications

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“…One category is supervised classification, including support vector machine (SVM) [12] and knearest neighbors (k-NN) [13]. The other category is unsupervised classification [14], including self-organization feature map (SOFM) [12] and fuzzy c-means [15]. While all these methods achieved good results, and yet the supervised classifier performs better than unsupervised classifier in terms of classification accuracy (success classification rate).…”

Section: Introductionmentioning

confidence: 99%

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Zhang¹,

Wu²

2012

PIER

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252

146

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Abstract-Automated and accurate classification of MR brain images is extremely important for medical analysis and interpretation. Over the last decade numerous methods have already been proposed. In this paper, we presented a novel method to classify a given MR brain image as normal or abnormal. The proposed method first employed wavelet transform to extract features from images, followed by applying principle component analysis (PCA) to reduce the dimensions of features. The reduced features were submitted to a kernel support vector machine (KSVM). The strategy of Kfold stratified cross validation was used to enhance generalization of KSVM. We chose seven common brain diseases (glioma, meningioma, Alzheimer's disease, Alzheimer's disease plus visual agnosia, Pick's disease, sarcoma, and Huntington's disease) as abnormal brains, and collected 160 MR brain images (20 normal and 140 abnormal) from Harvard Medical School website. We performed our proposed methods with four different kernels, and found that the GRB kernel achieves the highest classification accuracy as 99.38%. The LIN, HPOL, and IPOL kernel achieves 95%, 96.88%, and 98.12%, respectively. We also compared our method to those from literatures in the last decade, and the results showed our DWT+PCA+KSVM with GRB kernel still achieved the best accurate classification results. The averaged processing time for a 256 × 256 size image on a laptop of P4 IBM with 3 GHz processor and 2 GB RAM is 0.0448 s. From the experimental data, our method was effective and rapid. It could be applied to the field of MR brain image classification and can assist the doctors to diagnose where a patient is normal or abnormal to certain degrees.

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Section: Introductionmentioning

confidence: 99%

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Zhang¹,

Wu²

2012

PIER

Self Cite

252

146

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show abstract

“…The forward neural network (FNN) [13] was chosen as the classifier because it is a powerful tool among supervised classifiers and it can classify nonlinear separable patterns and approximate an arbitrary continuous function [14]. However, to find the optimal parameters of FNN is a difficult task because the search algorithms are easily trapped in local extrema.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

Zhang¹,

Wu²,

Wang³

2011

PIER

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168

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is a hot topic in the field of neuroimaging. Recently many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method first employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artificial bee colony (ABC) algorithm based on both fitness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artificial bee colony (SCABC). Moreover, the K-fold stratified cross validation was employed to avoid overfitting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20 times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classification accuracy.

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“…Neural networks are widely used in pattern classification since they do not need any information about the probability distribution and the a priori probabilities of different classes [26]. A single-hidden-layer backpropagation neural network is adopted with sigmoid neurons in the hidden layer and a linear neuron in the output layer.…”

Section: Our Proposed Algorithm-acpsomentioning

confidence: 99%

A Novel Method for Magnetic Resonance Brain Image Classification Based on Adaptive Chaotic Pso

Zhang¹,

Wang²,

Wu³

2010

PIER

Self Cite

117

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Abstract-Automated and accurate classification of magnetic resonance (MR) brain images is an integral component of the analysis and interpretation of neuroimaging. Many different and innovative methods have been proposed to improve upon this technology. In this study, we presented a forward neural network (FNN) based method to classify a given MR brain image as normal or abnormal. This method first employs a wavelet transform to extract features from images, and then applies the technique of principle component analysis (PCA) to reduce the dimensions of features. The reduced features are sent to an FNN, and these parameters are optimized via adaptive chaotic particle swarm optimization (ACPSO). K-fold stratified cross validation was used to enhance generalization. We applied the proposed method on 160 images (20 normal, 140 abnormal), and found that the classification accuracy is as high as 98.75% while the computation time per image is only 0.0452 s.

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Weights Optimization of Neural Network via Improved Bco Approach

Cited by 71 publications

References 23 publications

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

An MR Brain Images Classifier via Principal Component Analysis and Kernel Support Vector Machine

Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm

A Novel Method for Magnetic Resonance Brain Image Classification Based on Adaptive Chaotic Pso

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