Brain Tumor Classification

Abstract

The increasing prevalence of brain tumors, which are abnormal growths that occur inside the brain, is a significant problem in medicine. Brain tumor classification is a difficult task in the field of medical image processing. This is because manual categorization can also lead to incorrect diagnoses and forecasts. There is also the possibility that manually analyzing large volumes of data might be challenging. The foundations of effective treatment are a precise diagnosis and quick action, both of which are essential. This research aims to delve further into the topic of brain tumor classification using several techniques such as Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Logistic Regression (LG), and Convolutional Neural Network (CNN). Principally, this investigation aims to develop a dependable and precise system that can autonomously detect and classify many forms of brain tumors, such as pituitary tumors, gliomas, and meningioma, among others. The investigation utilized a diverse range of brain-derived magnetic resonance imaging datasets. This research evaluates the effectiveness of each algorithm by including performance parameters such as accuracy, precision, recall, and F1 score. This study concludes that CNN exhibits the highest accuracy, scoring 99.8 percent, after comparing the results of each discussed algorithm.