Segmentation and classification of medical big data on brain tumor using bacteria foraging optimization algorithm along with learning automata

Abstract

AbstractDetecting the early stage of brain tumors is significant for an effective therapy that can probably minimize the death rate of patients affected from brain tumors. Magnetic resonance imaging is the benchmark standard for diagnosing brain cancers but, it was difficult to split and categorize different kinds of brain tumors due to the delicate arrangement of the brain's anatomy. To overcome these difficulties and provide an effective classification, this research introduced a hybridized optimization technique which enhance the performance of the classifier. The hybridization is done between Bacteria Foraging Optimization Algorithm (BFOA) along with Learning Automata (LA), these two techniques improve the search speed and automate the learning capability of Convolutional Neural Network (CNN) classifier. The obtained results show that proposed BFOA LN‐CNN classifier better than the existing Deep Convolutional Neural Network (DCNN) based on Improved Harris Hawks Optimization (HHO) known as G‐HHO and Improved Invasive Bat (IIB)‐based Deep Residual network model. The classification accuracy of the proposed method is 99.41% whereas the DCNN‐G‐HHO and IIB deep residual neural network provides accuracy of 97% and 91.9% respectively.