Track papers across labs, topics, and trends.
Search papers, labs, and topics across Lattice.
This study introduces ORB-SVM, a hybrid framework that combines the Oriented FAST and Rotated BRIEF (ORB) algorithm for feature extraction with a Support Vector Machine (SVM) for classification, aimed at improving brain tumor detection from MRI scans. By achieving a remarkable data reduction of approximately 99.5%, the method effectively minimizes background noise while preserving essential diagnostic features, leading to a classification accuracy of 97.5% on the Br35H dataset. This approach addresses the computational inefficiencies of conventional deep learning models, offering a more resource-efficient solution for medical image analysis.
Achieving 97.5% accuracy in brain tumor detection while reducing data size by 99.5% could revolutionize MRI analysis in clinical settings.
Brain cancer remains one of the most significant challenges in modern medicine, where the accuracy of early stage diagnosis is a decisive factor in patient survival and treatment efficacy. Although Magnetic Resonance Imaging (MRI) is the established gold standard for visualizing neurological structures, the interpretation of these high dimensional scans is often complicated by subjective variability among practitioners and the inherent noise present in complex medical images. While contemporary approaches frequently rely on high parameter deep learning architectures, such models often involve significant computational costs and require extensive data for effective training. This study introduces a hybrid framework that utilizes the Oriented FAST and Rotated BRIEF (ORB) algorithm for precise feature extraction and a Support Vector Machine (SVM) for classification [15] [11]. The proposed approach achieves a substantial data reduction of approximately 99.5%, which effectively minimizes the influence of non informative background data while preserving critical diagnostic patterns essential for tumor identification. By balancing feature sparsity with a robust kernel based classifier, this methodology addresses the limitations of over parameterized systems while maintaining high diagnostic integrity. Experimental evaluations conducted on the Br35H dataset demonstrate that the framework attains a classification accuracy of 97.5%. The findings suggest that the integration of localized feature representation and optimized classification provides a reliable and resource efficient alternative for medical image analysis, offering a structured solution that maintains performance without the need for extensive computational overhead.