Intelligent Brain Tumor Detection System Using SVM and MRI Analysis

Abstract

Brain tumors are one of the most life-threatening diseases that require early detection and accurate diagnosis. Magnetic
resonance imaging (MRI) is the most commonly used non-invasive technique for brain tumor detection. In this paper, we
present a comparative study of pituitary, glioma, and meningioma tumor detection using support vector machines (SVMs)
from MRI images. SVM is a machine learning algorithm used for classification and regression analysis, which has been
shown to be effective in various medical image analysis applications. In this study, we used SVM to classify different types
of brain tumors based on their MRI images. The study was conducted on a dataset of 150 MRI images, including 50 pituitary,
50 glioma, and 50 meningioma tumors. The performance of the SVM algorithm was evaluated using various metrics such as
sensitivity, specificity, accuracy, and F1-score. Our experimental results show that SVM is a promising technique for brain
tumor detection and classification, achieving an overall accuracy of 90.5% for pituitary tumors, 91.2% for glioma tumors,
and 89.5% for meningioma tumors. Furthermore, we also compared the performance of SVM with other machine learning
algorithms, such as random forest and K-nearest neighbors. The results show that SVM outperforms the other algorithms in
terms of accuracy, sensitivity, and specificity.