DESIGN AND IMPLEMENTATION OF BRAIN TUMOR DETECTION USING HYBRID MACHINE LEARNING

Abstract

Abstract: The brain tumors detection is crucial for early diagnosis and treatment, which can significantly improve patient outcomes. This paper presents a novel approach for brain tumor detection using a hybrid machine learning model that combines Support Vector Machine (SVM) and Random Forest (RF). The SVM is employed for classification, leveraging its ability to find the optimal boundary for tumor classification in the feature space. On the other hand, the Random Forest algorithm is used to enhance the model's accuracy by handling a large dataset and extracting relevant features. The hybrid SVM+RF model is trained on medical imaging data, such as MRI scans, to identify and classify tumor regions as benign or malignant. The performance of the proposed system is evaluated based on metrics such as accuracy, precision, recall and F1-score, demonstrating superior classification performance compared to traditional machine learning methods. This approach provides an efficient, scalable and reliable solution for automated brain tumor detection, offering potential clinical applications in real-time diagnostics and decision-making processes.