Immune Feature Weighted Least-Squares Support Vector Machine for Brain Tumor Detection Using MR Images

ABSTRACT

Brain tumor is one of the leading causes of death making tumor detection very important and challenging in the medical field. This paper describes tumor detection in medical images using immune feature weighted least squares-support vector machine (IFWLS-SVM). The challenge in brain tumor detection in magnetic resonance (MR) images is the existence of non-linearity in real data. Least squares-support vector machine (LS-SVM) is a conventional algorithm that has been applied to diagnose the detection problems in MR images and non-linear distribution in brain tumors. LS-SVM solves a linear system for a training algorithm instead of using quadratic programming in SVM. In conventional LS-SVM, each sample feature taken has equal importance for classification results, which does not give accurate results in real applications. In addition, parameters of LS-SVM and their kernel function prominently affect the classification result. An IFWLS-SVM has been used to optimize the kernel and tune the parameters of LS-SVM in this paper. Theoretical analysis and experimental results showed that IFWLS-SVM has better performance than other conventional algorithms.

KEYWORDS:

• Astrocytoma
• Brain tumor
• Immune algorithm (IA)
• Least-squares
• Magnetic resonance imaging (MRI)
• Optimization
• Support vector machine (LS-SVM)

ACKNOWLEDGMENTS

The authors thank Bharat Scans Education and Research Foundation for giving full support in providing medical information and real data for research purposes.

DISCLOSURE STATEMENT

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

R. Preetha

R. Preetha received her Bachelor of Engineering degree in electronics and communication from Madurai Kamaraj University in 2003, Master of Engineering degree in communication systems from Anna University – Chennai in 2011. She has been working as an assistant professor in the same field for 8 years. Currently, she is pursuing PhD in information and communication engineering from Anna University since 2012. Her area of interest includes Image Processing, Medical Image Processing and Digital Image Processing. She is a member of IEEE, ISTE and IETE.

E-mail: [email protected]

R. Bhanumathi

R. Bhanumathi received her Bachelor of Engineering degree in computer science from S.A. Engineering College in 2009, Master of Engineering degree in computer science from Prathyusha Institute of Technology and Management in 2011. She is currently working as an assistant professor in Apollo Priyadarshanam Institute of Technology. She is a research scholar in the faculty of Information and Communication Engineering from Anna University, Chennai. Her area of interest includes Image Processing, Medical Image Processing and Digital Image Processing.

E-mail: [email protected]

G.R. Suresh

G. R. Suresh received his Bachelor of Engineering degree in electronics and communication from Manonmaniam Sundaranar University in 1997, Master of Engineering degree in communication systems from Madurai Kamaraj University, Madurai in 2000 and PhD degree in the Faculty of Information and Communication Engineering from Anna University, Chennai in 2010. He is currently working as a professor in Easwari Engineering College, Chennai. He has more than 18 years of experience in teaching at under graduate and graduate level. He has published more than 60 research papers in journals and conferences. He has received the computer engineering division prize from the Institution of Engineers (India) for the year 2009. He is an active reviewer in the journals, IET image Processing and International journal of Electronics. His research area includes Medical Signal & Image processing, Speech processing and WSN in Telemedicine. He is a member of IEEE, IET and life member of ISTE.

E-mail: [email protected]