An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine
Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wa...
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| Format: | Article |
| Language: | English |
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Wiley
2013-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2013/130134 |
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| author | Yudong Zhang Shuihua Wang Genlin Ji Zhengchao Dong |
| author_facet | Yudong Zhang Shuihua Wang Genlin Ji Zhengchao Dong |
| author_sort | Yudong Zhang |
| collection | DOAJ |
| description | Automated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA) to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM) with RBF kernel, using particle swarm optimization (PSO) to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM. |
| format | Article |
| id | doaj-art-d32564867b6248d1bfb5721075f582ec |
| institution | Kabale University |
| issn | 1537-744X |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d32564867b6248d1bfb5721075f582ec2025-02-03T01:12:56ZengWileyThe Scientific World Journal1537-744X2013-01-01201310.1155/2013/130134130134An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector MachineYudong Zhang0Shuihua Wang1Genlin Ji2Zhengchao Dong3School of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, ChinaSchool of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, ChinaSchool of Computer Science and Technology, Nanjing Normal University, Nanjing, Jiangsu 210023, ChinaBrain Imaging Lab and MRI Unit, New York State Psychiatry Institute and Columbia University, New York, NY 10032, USAAutomated abnormal brain detection is extremely of importance for clinical diagnosis. Over last decades numerous methods had been presented. In this paper, we proposed a novel hybrid system to classify a given MR brain image as either normal or abnormal. The proposed method first employed digital wavelet transform to extract features then used principal component analysis (PCA) to reduce the feature space. Afterwards, we constructed a kernel support vector machine (KSVM) with RBF kernel, using particle swarm optimization (PSO) to optimize the parameters C and σ. Fivefold cross-validation was utilized to avoid overfitting. In the experimental procedure, we created a 90 images dataset brain downloaded from Harvard Medical School website. The abnormal brain MR images consist of the following diseases: glioma, metastatic adenocarcinoma, metastatic bronchogenic carcinoma, meningioma, sarcoma, Alzheimer, Huntington, motor neuron disease, cerebral calcinosis, Pick’s disease, Alzheimer plus visual agnosia, multiple sclerosis, AIDS dementia, Lyme encephalopathy, herpes encephalitis, Creutzfeld-Jakob disease, and cerebral toxoplasmosis. The 5-folded cross-validation classification results showed that our method achieved 97.78% classification accuracy, higher than 86.22% by BP-NN and 91.33% by RBF-NN. For the parameter selection, we compared PSO with those of random selection method. The results showed that the PSO is more effective to build optimal KSVM.http://dx.doi.org/10.1155/2013/130134 |
| spellingShingle | Yudong Zhang Shuihua Wang Genlin Ji Zhengchao Dong An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine The Scientific World Journal |
| title | An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine |
| title_full | An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine |
| title_fullStr | An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine |
| title_full_unstemmed | An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine |
| title_short | An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine |
| title_sort | mr brain images classifier system via particle swarm optimization and kernel support vector machine |
| url | http://dx.doi.org/10.1155/2013/130134 |
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