Brain MRI Slices Classification Using Least Squares Support Vector Machine
International Journal of Intelligent Computing in Medical Sciences & Image Processing
First page number:
Last page number:
This research paper proposes an intelligent classification technique to identify normal and abnormal slices of brain MRI data. The manual interpretation of tumor slices based on visual examination by radiologist/physician may lead to missing diagnosis when a large number of MRIs are analyzed. To avoid the human error, an automated intelligent classification system is proposed which caters the need for classification of image slices after identifying abnormal MRI volume, for tumor identification. In this research work, advanced classification techniques based on Least Squares Support Vector Machines (LS-SVM) are proposed and applied to brain image slices classification using features derived from slices. This classifier using linear as well as nonlinear Radial Basis Function (RBF) kernels are compared with other classifiers like SVM with linear and nonlinear RBF kernels, RBF classifier, Multi Layer Perceptron (MLP) classifier and K-NN classifier. From this analysis, it is observed that the proposed method using LSSVM classifier outperformed all the other classifiers tested.
Artificial intelligence; Classification--Computer programs; Image analysis--Data processing; Least squares--Computer programs; Magnetic resonance imaging
Artificial Intelligence and Robotics | Bioimaging and Biomedical Optics | Controls and Control Theory | Diagnosis | Radiology | Signal Processing | Systems and Communications
Use Find in Your Library, contact the author, or use interlibrary loan to garner a copy of the article. Publisher copyright policy allows author to archive post-print (author’s final manuscript). When post-print is available or publisher policy changes, the article will be deposited
Thamarai Selvi, S.,
Gewali, L. P.
Brain MRI Slices Classification Using Least Squares Support Vector Machine.
International Journal of Intelligent Computing in Medical Sciences & Image Processing, 1(1),