Enhancing Brain Tumor Diagnosis: A Synergistic Approach with Support Vector Machines and Decision Trees for Improved Detection and Segmentation

Abstract

Abstract: Brain tumor identification and segmentation are critical components of modern medical imaging, facilitating timely diagnoses and effective therapy planning. This paper introduces an innovative methodology that harmoniously integrates Support Vector Machines (SVM) and Decision Tree methods. SVM adeptly classifies brain images into tumor and nonneoplastic regions, harnessing its proficiency with complex datasets. Decision Tree methods complement this process, providing transparent segmentation that accurately delineates intricate tumor boundaries. The synergy between SVM and Decision Tree methods establishes a robust framework for brain tumor identification. This novel approach holds promise for enhanced diagnosis speed, particularly valuable in time-sensitive medical scenarios. Emphasizing potential advancements in patient care outcomes, the methodology enables tailored treatment plans with greater precision. Positioned at the core of brain image analysis, this novel methodology signifies a substantial step forward in healthcare practices, fostering personalized approaches to brain tumor diagnosis and treatment.