Brain Tumor Detection by Modified Particle Swarm Optimization Algorithm and Multi-Support Vector Machine Classifier

Abstract

In recent times, brain tumor detection become an important task in medical image processing applications. The early detection of brain tumor improves the treatment process and increases the survival rate of the patients. However, the manual segmentation and classification of brain tumor is a complex and time consuming process. Therefore, a new automatic brain tumor detection model is implemented in this manuscript for effective brain tumor detection. After collecting the scans from the cloud, the normalization and adaptive histogram equalization techniques are used to enhance the acquired brain scan quality. Further, the tumor regions are segmented by integrating the Adaptive Kernel Fuzzy C Means (AKFCM) clustering algorithm with the Otsu thresholding technique. Next, the deep and textual feature values are extracted from the segmented regions using the Gray Level Co-occurrence Matrix (GLCM), Local Ternary Pattern (LTP), and LeNet-5. The dimension of the extracted feature vectors is optimized by using the Modified Particle Swarm Optimization (MPSO) algorithm, which are given as the input to the Multi-Support Vector Machine (MSVM) for tumor classification. The experimental outcomes confirmed that the MPSO-MSVM model obtained high accuracy of 98.89%, which is superior related to the existing machine learning techniques.