Mathematical Modelling for Brain Tumor Segmentation and Classification Using Machine Learning

Abstract

Brain tumors are life-threatening diseases that need to be diagnosed quickly and correctly in order to be treated effectively. Traditional MRI scans can help find problems, but they take a long time, are prone to mistakes, and need to be done by a doctor who knows what they're doing. Machine learning (ML) techniques offer advanced, automated ways to deal with these problems by dividing and classifying brain tumors. This research looks into the mathematical models that make these processes work and shows how they can be used to find brain tumors. The segmentation process starts with an MRI picture. The goal is to separate brain tissue that is tumor-like from brain tissue that is healthy. Using machine learning techniques to find tumor pixels is like solving an optimization problem from a mathematical point of view. A number of methods are used to achieve accurate segmentation, including thresholding, K-means grouping, and convolutional neural networks (CNNs). These models utilize concentrated values, angles, and design characteristics taken from MRI pictures to form it simpler to discover the edges of tumors. The MRI picture is turned into a high-dimensional include space amid highlight extraction so that the development can be classified. Support Vector Machines (SVMs), random forest, and Deep Neural Systems (DNNs) are a few cases of machine learning models that are utilized to sort tumors into bunches, such as typical or unsafe. To work, these models discover the leading hyperplane, construct choice trees, or utilize neural organize layers to associate information to lesson names. This inquire about appears that utilizing both scientific modeling and machine learning together makes brain tumor division and classification much more exact and fast. A lot of progress has been made, but problems like hard-to-understand models and a lack of data still exist. This shows that more study is needed to make these models better for use in clinical settings.