Performance Analysis of Glioma Brain Tumor Segmentation Using CNN Deep Learning Approach

Abstract

The segmentation of Glioma tumor regions in brain Magnetic Resonance Imaging (MRI) image, using Convolutional Neural Networks (CNN) classification method, is proposed in this paper. The adaptive histogram equalization method is applied on the brain MRI image for enhancing the abnormal pixels with respect to surrounding pixels. This enhanced brain image is transformed into multidirectional scaling image using Gabor transform. Then, features are extracted from this multidirectional scaling image and then these features are trained and classified using CNN deep learning algorithm in order to differentiate the Glioma from normal brain MRI image. Finally, the tumor regions are segmented using morphological operations. The proposed Glioma brain tumor segmentation method using CNN classification approach obtains 96.9% of sensitivity, 99.3% of specificity and 99.2% of accuracy.

KEYWORDS:

• Brain
• Classifications
• Glioma
• Segmentation
• Tumors

Additional information

Notes on contributors

M. Tamilarasi

M Tamilarasi completed her PhD in Anna University, Chennai. She received her MTech degree from National Institute of Technology, Tiruchirappalli, Tamil Nadu. She has 22 years of teaching experience starting from lecturer to professor in various engineering colleges around Tamil Nadu, India. At present she is working as professor in Gnanamani College of Technology, Namakkal, Tamil Nadu, India.