An MR Brain Images Classifier System via Particle Swarm Optimization and Kernel Support Vector Machine

• Published in: TheScientificWorld Vol. 2013; no. 2013; pp. 1 - 9
• Main Authors: Zhang, Yudong, Wang, Shuihua, Ji, Genlin, Dong, Zhengchao
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2013; Hindawi Limited
• Subjects: Accuracy; Advantages; Algorithms; Artificial Intelligence; Biomedical research; Borrelia; Brain; Brain - metabolism; Brain - pathology; Brain research; Classification; Dementia disorders; Digital imaging; Encephalitis; Feature extraction; Humans; Hybrid systems; Image classification; Image Processing, Computer-Assisted - standards; Kernels; Magnetic resonance imaging; Medical imaging; Methods; Multiple sclerosis; NMR; Nuclear magnetic resonance; Optimization algorithms; Parameters; Particle swarm optimization; Principal Component Analysis; Principal components analysis; Sarcoma; Studies; Support Vector Machine; Support vector machines; Toxoplasmosis; Wavelet analysis; Wavelet transforms; Websites
• ISSN: 2356-6140; 1537-744X
• DOI: 10.1155/2013/130134

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.