发表机构
Shahid Beheshti University（沙希德·贝赫什提大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出ORB-SVM混合框架，以ORB提取特征、SVM分类，在Br35H数据集上实现97.5%的脑肿瘤MRI检测准确率，数据缩减约99.5%，兼具高效性与高诊断完整性。
AI 中文摘要
脑癌仍是现代医学面临的重大挑战之一，早期诊断的准确性是决定患者生存和治疗效果的关键因素。尽管磁共振成像（MRI）是可视化神经结构的既定金标准，但这些高维扫描的解读常因从业者间的主观差异以及复杂医学图像中固有的噪声而变得复杂。当前方法常依赖高参数深度学习架构，此类模型往往涉及显著的计算成本，且需要大量数据才能有效训练。本研究引入一种混合框架，利用定向FAST和旋转BRIEF（ORB）算法进行精确特征提取，并采用支持向量机（SVM）进行分类。所提方法实现了约99.5%的大幅数据缩减，有效最小化了无信息背景数据的影响，同时保留了肿瘤识别所必需的关键诊断模式。通过平衡特征稀疏性与基于鲁棒核的分类器，该方法解决了过参数化系统的局限性，同时保持了高诊断完整性。在Br35H数据集上开展的实验评估表明，该框架达到了97.5%的分类准确率。研究结果表明，局部特征表示与优化分类的结合为医学图像分析提供了一种可靠且资源高效的替代方案，提供了一种无需大量计算开销即可保持性能的结构化解决方案。
英文摘要
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 [1], [2]. The proposed approach achieves a sub- stantial 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 per- formance without the need for extensive computational overhead.
Comments6 pages , 7 figures
DOI：10.1109/IICAI70155.2026.11620776
