ENHANCED BRAIN TUMOR DETECTION IN MRI: A COMPARATIVE STUDY OF MACHINE LEARNING MODELS

Abstract

Image processing is essential and attractive in the medical and healthcare. Digital image processing identifies diverse pathological methods, like identifying, classifying, evaluating, and testing brain tumors through microscopic images. Many machine-learning methods are recognized in the era of the AI century for detecting brain tumors through Magnetic Resonance Imaging (MRI). MRI is a recognized image processing method through three-dimensional examination, which identifies unambiguous images of the infection or tumor. The paper aims to offer supervised machine-learning algorithms for brain tumor detection in MRI images through a comparative analysis of different models. Considering the specific features of the tumor and surrounding infected tissues of the brain through  analysis supports us in estimating the accuracy of the models and recognizing the optimal operative method. In this paper, four supervised machine learning models are considered: Logistic Regression (LR), Neural Network (NN), Stochastic Gradient Descent (SGD), and Support Vector Machines (SVM). MRI images can quickly identify brain tumors or infections by comparing these models. Furthermore, a model is developed using the Visual Geometry Group (VGG-19) embedder and the Kaggle dataset. The result section shows that the proposed model outperforms the benchmark schemes by attaining high proximity accuracies.