Performance Analysis of Meningioma Brain Tumor Detection System Using Feature Learning Optimization and ANFIS Classification Method

Abstract

The meningioma tumors are classified and segmented using soft computing methods in this paper. The noise contents are detected and reduced using directional filters and then Gabor transform is applied on this noise smoothed brain image for transforming the spatial pixels into multi resolution pixels. Further, features are derived from this Gabor transformed multi resolution image and these are optimized using ant feature learning optimization algorithm. These optimized features are classified using Adaptive Neuro Fuzzy Inference System (ANFIS) classification approach and then morphological segmentation method is applied on this classified abnormal meningioma brain image in order to segment the tumor regions. The proposed meningioma tumor detection system obtains 98.1% of sensitivity, 99.75 of specificity, 99.6% of accuracy, 98.55 of precision, 97.95 of F1-Score, and 98.1% of relevance factor.

KEYWORDS:

• Classification
• Features
• Meningioma
• Optimization
• Tumors

Acknowledgements

The authors would like to thank their friends and colleagues for their constant help and support throughout the study and to obtain the results.

Additional information

Notes on contributors

J. Jasmine Hephzipah

J Jasmine Hephzibah has received the bachelor's degree in electronics and communication engineering from Anna University, Chennai, in the year 2005. Her master's degree is in applied electronics completed in the year 2010, from Anna University, Trichy, Tamil Nadu, India. She is pursuing her PhD, degree in the Faculty of Information and Communication Engineering, Anna University, Chennai, Tamil Nadu, India. She worked as an assistant professor in the field of electronics and communication in Sudharsan Engineering College, Trichy from 2005 to 2011. She is working as an assistant professor in RMK Engineering College from 2011. Her research interest includes image processing and telecommunication network. She is a life member of the Indian Society for Technical Education (ISTE). She is single point of contact (SPOC) of Telecom Center of Excellence in RMK Engineering College and single point of contact (SPOC) of Telecom Sector Skill Council (TSSC), Government of India. She has published few international journals and presented many papers in national and international Conferences. She has been honoured as Best Faculty Award in Sudharsan Engineering College in the year 2009. Corresponding author. Email: [email protected]

P. Thirumurugan

P Thirumurugan has received the bachelor's degree in electronics and communication engineering from Anna University, Chennai, in the year 2005. His master's degree is in applied electronics completed in the year 2007, from Anna University, Chennai, Tamil Nadu, India. He has completed his PhD, degree in the Faculty of Information and Communication Engineering, Anna University, Chennai, Tamil Nadu, India. He worked as an assistant professor in the field of electronics and communication in RVS College of Engineering and Technology, Dindigul from 2007 to 2011. He is now working as an associate professor in PSNA College of Engineering and Technology, Dindigul, Tamil Nadu, India. His research interest includes image processing and wireless sensor network. He is a life member of the Indian Society for Technical Education. He has published more than 40 articles in the international journals and presented many papers in national and international conferences. He has been honoured as Best Teaching Faculty -Dr A P J Abdul Kalam Educational Trust award for his abiding teaching interest and excellence in the year 2019. Email: [email protected]