[2603.28357] Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images

Computer Science > Computer Vision and Pattern Recognition arXiv:2603.28357 (cs) [Submitted on 30 Mar 2026] Title:Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images Authors:Ha Anh Vu View a PDF of the paper titled Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images, by Ha Anh Vu View PDF HTML (experimental) Abstract: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. Subjects: Computer Vision ...