Quantum squirrel search algorithm based support vector machine algorithm for brain tumor classification

Abstract

AbstractA Brain tumor is growth or mass of irregular cells inside your brain, several various kinds of brain tumors survive. A few brain tumors are cancerous (malignant), also various brain tumors are noncancerous (benign). The existing approach faces problem related to local optima issues, complexity in computational time, less convergence speed and less exploration ability. The stimulated quality Selection of Quantum Squirrel Search Algorithm (QSSA) is based on equally appearance with methylation information of prostate cancer. Issues with multiple models, multiple dimensions, and unimodal optimization are all addressed by this QSSA concept. The input image of the CE‐MRI dataset consists of 3064 segments with comprise (708 slices) meningiomas, (1426 slices) gliomas and (930 slices) pituitary tumors. In order to extract appropriate data from an image, a convolutional neural network (CNN) executes a number of mathematical processes, including convolutions and pooling. The CNN model's benefits include a large number of important features that can be extracted and good accuracy. Then, Support Vector Machine (SVM), a machine learning technique used for supervised learning, is typically associated within the double classification. The SVM model benefits from a large effective dimensional space and adequate memory. The proposed QSSA has obtained high Accuracy 98.3%, Sensitivity 95.4% and Specificity 97.9% than existing Correlation Learning Mechanism (CLM) which has 90.4% accuracy, 86% sensitivity and 91.5% specificity respectively.