Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images

• URL: http://arxiv.org/abs/2603.28357v1
• Date: Mon, 30 Mar 2026 12:24:54 GMT
• Title: Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images
• Abstract summary: This paper presents a weighted ensemble learning approach that combines deep learning and traditional machine learning models to improve classification performance.<n>A weighted voting mechanism assigns higher influence to models with better individual accuracy, ensuring robust decision-making.<n> Experimental evaluations on the Figshare and Kaggle MRI datasets demonstrate that the proposed method achieves state-of-the-art accuracy.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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