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Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images

Ha Anh Vu

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.

Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images

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.

Paper Structure

This paper contains 24 sections, 1 equation, 4 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Single Classifiers
  4. Ensemble Learning
  5. METHODOLOGY
  6. MRI data representations
  7. Grayscale images
  8. Histogram of Oriented Gradients (HOG)
  9. Edge images
  10. Classifiers
  11. K-Nearest Neighbors (KNN)
  12. Support Vector Machines (SVM)
  13. CNN-MRI
  14. DensNet121
  15. ResNet
  16. ...and 9 more sections

Figures (4)

  • Figure 1: Edge detection process on MRI image: (a) Original (b) Contrasted (c) Segmented (d) Edge detected.
  • Figure 2: The CNN-MRI architecture used in the experiments.
  • Figure 3: The voting system overview.
  • Figure 4: Sample images representing each tumor class: glioma, meningioma, pituitary, and no tumor.