Brain Tumor Classification and Prediction Using Deep Learning

Abstract

Brain tumor categorization and prediction benefit from the use of Support Vector Machines (SVM) and Convolutional Neural Networks (CNN). SVM algorithms effectively partition labeled brain pictures into tumor kinds by identifying patterns and attributes. CNN algorithms independently learn representations from brain images to predict tumor presence. Performance is measured using criteria including accuracy, sensitivity, specificity, and precision. CNNs have also proved beneficial for brain tumor prediction. These deep learning methods develop complicated representations from vast datasets of brain scans, predicting tumor presence or absence. Evaluation criteria including as accuracy, precision, recall, and F1-score quantify the efficacy of CNN models in predicting brain tumor status. Further study is required for greater performance and bigger datasets. Combining SVM for classification with CNN for prediction gives a hybrid technique for brain tumor analysis. SVM algorithms categorize brain scans into tumor kinds, while CNN models identify tumor presence within these groupings. This combination technique delivers precise information on brain tumor categorization and prediction, boosting the accuracy of diagnosis and treatment planning. Continued research will further develop these algorithms for better brain cancer assessment.