Brain Tumor Classification Using Hybrid Model Of PSO And SVM Classifier

Medical image processing is the very significant process for any disease diagnosis now a days. MRI is usually used to detect the presence of tumor and its type .The process which is followed in classification of brain tumor is very complicated. There are various steps for the medical image processing like image segmentation, image extraction and image classification. Different types of features are extracted from the segmented MRI images like intensity, shapes and texture based features. The feature selection approach is used to select the small subset of features from MRI image which minimize redundancy and maximize relevance to the target. In this paper, online database of MRI images containing brain tumor is taken then a machine learning model is developed by using the Particle Swarm Optimization(PSO) algorithm for feature selection and then Support Vector Machine(SVM) classifier is used to classify the type of tumor in present brain MRI images.

[1]  Arslan Shaukat,et al.  Hybrid Feature Selection and Tumor Identification in Brain MRI Using Swarm Intelligence , 2013, 2013 11th International Conference on Frontiers of Information Technology.

[2]  K. Deb,et al.  Reliable classification of two-class cancer data using evolutionary algorithms. , 2003, Bio Systems.

[3]  Harikumar Rajaguru,et al.  Oral cancer classification from hybrid ABC-PSO and Bayesian LDA , 2017, 2017 2nd International Conference on Communication and Electronics Systems (ICCES).

[4]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[5]  M. Karnan,et al.  Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques , 2010, 2010 IEEE International Conference on Computational Intelligence and Computing Research.

[6]  Xiongxiong He,et al.  Auxiliary Diagnosis of Breast Tumor Based on PNN Classifier Optimized by PCA and PSO Algorithm , 2017, 2017 9th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC).

[7]  Russell C. Eberhart,et al.  A discrete binary version of the particle swarm algorithm , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[8]  Muhammad Hussain,et al.  Enhancing SVM performance in intrusion detection using optimal feature subset selection based on genetic principal components , 2014, Neural Computing and Applications.

[9]  P. Sathyanarayana,et al.  Image Texture Feature Extraction Using GLCM Approach , 2013 .

[10]  Yudong Zhang,et al.  AN MR BRAIN IMAGES CLASSIFIER VIA PRINCIPAL COMPONENT ANALYSIS AND KERNEL SUPPORT , 2012 .

[11]  Vasudev Mohan,et al.  Kernel-based PSO and FRVM: An automatic plant leaf type detection using texture, shape, and color features , 2016, Comput. Electron. Agric..

[12]  H. Hannah Inbarani,et al.  Hybrid Tolerance Rough Set-Firefly based supervised feature selection for MRI brain tumor image classification , 2016, Appl. Soft Comput..

[13]  V. P. Gladis Pushpa Rathi,et al.  Brain tumor MRI image classification with feature selection and extraction using linear discriminant analysis , 2012, ArXiv.

[14]  Mengjie Zhang,et al.  Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach , 2013, IEEE Transactions on Cybernetics.