Detection and Classification of MRI-Based Brain Tumor via Jaya Algorithm and Twin Support Vector Machine

Abstract

Brain tumor detection and classification is one of the challenging tasks in the medical image application. Early detection of a brain tumor can help diagnosis and treatment of the patients. Magnetic Resonance Imaging (MRI) is widely used for the detection of brain tumor. Manual analysis of brain MRI, and classification of brain tumor is a tedious and time-consuming job. This paper introduces a novel approach to brain tumor segmentation and classification using BRATS 2015 datasets. Our system exploits the benefits of Jaya Algorithm (JA) as an optimization technique for finding multi-level thresholds to segment the tumor part from the MRI. Feature extraction is implemented by Gray Level Co-occurrence Matrix (GLCM), followed by Principal Component Analysis (PCA) for feature reduction. Due to its inherent distinct features and advantages, a machine-learning approach, Twin Support Vector Machine (TSVM) is used as a classifier. The prediction accuracy of the proposed system yielded up to 97.89 % with sensitivity 96.48%, 98.97 precision, 97.91% F1 Score, and 0.0798 MSE. The accuracy, sensitivity, F1 Score, and MSE are found comparable to the other state-of-arts machine learning methods.