融合多模型加权投票，提升脑肿瘤MRI分类准确率。
Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images
- 用加权投票融合深度与传统机器学习模型，权重按单个模型精度分配。
- 在Figshare和Kaggle数据集上达到当前最优准确率，显著超越现有方法。
- 适合医疗影像分析、辅助诊断系统研发人员参考使用。
从MRI扫描中准确分类脑肿瘤对有效诊断和治疗规划至关重要。本文提出一种结合深度学习与传统机器学习的加权集成学习方法，以提升分类性能。该系统整合了ResNet101、DenseNet121、Xception、CNN-MRI、ResNet50（使用边缘增强图像）、SVM及KNN（使用HOG特征）等多个分类器，并通过加权投票机制赋予更高精度模型更大影响力，确保决策稳健。图像处理技术如平衡对比度增强、K均值聚类和Canny边缘检测被用于提升特征提取效果。在Figshare和Kaggle MRI数据集上的实验评估表明，所提方法实现了领先水平的准确率，优于现有模型。研究结果凸显了基于集成学习在脑肿瘤分类中的潜力，为医学图像分析提供了一个可靠且可扩展的框架。
原文摘要 · Abstract (English)
The accurate classification of brain tumors from MRI scans is essential for effective diagnosis and treatment planning. This paper presents a weighted ensemble learning approach that combines deep learning and traditional machine learning models to improve classification performance. The proposed system integrates multiple classifiers, including ResNet101, DenseNet121, Xception, CNN-MRI, and ResNet50 with edge-enhanced images, SVM, and KNN with HOG features. A weighted voting mechanism assigns higher influence to models with better individual accuracy, ensuring robust decision-making. Image processing techniques such as Balance Contrast Enhancement, K-means clustering, and Canny edge detection are applied to enhance feature extraction. Experimental evaluations on the Figshare and Kaggle MRI datasets demonstrate that the proposed method achieves state-of-the-art accuracy, outperforming existing models. These findings highlight the potential of ensemble-based learning for improving brain tumor classification, offering a reliable and scalable framework for medical image analysis.
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