A Hybrid Machine Learning Approach for Brain Tumor Classification Using Artificial Neural Network and Particle Swarm Optimization

Abstract

Brain tumors have an increasing trend in recent years and are one of the main causes of death. Therefore, computer-assisted secondary tools that can help diagnose brain tumors at an early stage are needed. It is crucial to use machine learning methods which can help brain tumors classification. In this paper, a hybrid machine learning approach is proposed for brain tumor classification using particle swarm optimization and artificial neural network on magnetic resonance (MR) images. The approach is composed of six steps. The first step includes enhancement of MR images and the second step consists of eliminating the skull region. The third step is composed of extracting the region of interest through segmentation of the masses with s-FCM method. In the fourth step, feature extraction of the segmented tumors is undertaken with four different methods and feature selection is applied with relief-f and sequential floating forward selection (SFFS) methods in fifth step. In the last step, benign and malignant tumors are classified using Bayes method, artificial neural networks (ANN), support vector machines (SVM) and particle swarm optimization-based artificial neural networks (PSO-ANN) classification methods and the results are compared with each other. In the experimental studies, the proposed PSO-ANN approach provides high scores such as 96.28% accuracy, 97.58% sensitivity, 93.75% specifity for brain tumor classification. As a result, the proposed approach can facilitate the decision making of radiologists for brain tumor classification. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.