Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images

Ha Anh Vu

cs.CVMar 30, 2026

Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images

Authors: Ha Anh Vu

Organizations: Artificial Intelligence Department, FPT University, Ho Chi Minh City, Vietnam

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.

Figures & tables

Panel of Figure 1 — Fig. 1 : Edge detection process on MRI image: (a) Original (b) Contrasted (c) Segmented (d) Edge detected.

Fig. 2 : The CNN-MRI architecture used in the experiments.

Panel of Figure 4 — Fig. 4 : Sample images representing each tumor class: glioma, meningioma, pituitary, and no tumor.

Model	Figshare Accuracy (%)	Kaggle Accuracy (%)
KNN	96.41	97.94
SVM	95.54	96.03
ResNet50	96.63	98.78
Xception	97.93	99.46
CNN-MRI	95.65	98.32
DenseNet121	98.15	99.60

TABLE I : Classifier performance on Figshare and Kaggle datasets.

Class	Figshare				Kaggle
Class	P	R	F1	#	P	R	F1	#
Glioma	0.97	0.98	0.97	428	1.00	0.99	0.99	300
Meningioma	0.94	0.93	0.94	213	0.99	0.99	0.99	306
Pituitary	0.98	0.98	0.99	279	0.99	0.99	1.00	300
No tumor	–	–	–	–	0.99	1.00	0.99	405
Accuracy: Figshare = 99.13%, Kaggle = 99.54%

TABLE II : Classification scores for the no-weight scenario on Figshare and Kaggle datasets.

Class	Figshare				Kaggle
Class	P	R	F1	#	P	R	F1	#
Glioma	0.98	0.98	0.97	428	1.00	0.99	0.99	300
Meningioma	0.97	0.97	0.98	213	0.98	1.00	0.99	306
Pituitary	0.98	0.98	0.99	279	0.99	0.99	1.00	300
No tumor	–	–	–	–	1.00	1.00	1.00	405
Accuracy: Figshare = 98.36%, Kaggle = 99.54%

TABLE III : Classification scores for the incremental scenario on Figshare and Kaggle datasets.

Class	Figshare				Kaggle
Class	P	R	F1	#	P	R	F1	#
Glioma	0.97	0.98	0.97	428	1.00	0.99	1.00	300
Meningioma	0.94	0.93	0.94	213	0.99	1.00	0.99	306
Pituitary	0.98	0.98	0.99	279	1.00	1.00	1.00	300
No tumor	–	–	–	–	1.00	1.00	1.00	405
Accuracy: Figshare = 99.13%, Kaggle = 99.69%

TABLE IV : Classification scores for the highest weight scenario on Figshare and Kaggle datasets.

Scenario	SVM	KNN	ResNet50	Xception	CNN-MRI	DenseNet121	ResNet101
1	1	3	1	3	2	1	4
2	1	1	0	1	1	1	2
3	1	2	1	1	0	1	2

TABLE V : Top three scenarios achieving 99.46% accuracy on the Figshare dataset.

Scenario	SVM	KNN	ResNet50	Xception	CNN-MRI	DenseNet121	ResNet101
1	1	1	1	4	2	4	2
2	0	0	1	2	1	2	1
3	0	0	1	3	1	2	1

TABLE VI : Top three scenarios achieved an accuracy of 99.85% running on Kaggle dataset.

Method	Single Classifiers	Acc. (%)
Khan et al. [ 3 ]	23-layer CNN	97.80
Shnaka et al. [ 2 ]	R-CNN	94.60
Momina et al. [ 38 ]	ResNet50	95.9
Montoya et al. [ 39 ]	ResNet50	97.30
Vu et al. [ 18 ]	ResNet50	96.53
Method	Ensemble System	Acc. (%)

TABLE VII : Performance comparison of the proposed method with existing approaches on the Figshare dataset.

Method	Single Classifiers	Acc. (%)
Asiri et al. [ 40 ]	CNN	94.58
Ishaq et al. [ 8 ]	EfficientNet	97.40
Rasheed et al. [ 4 ]	CNN	97.85
Rasheed et al. [ 5 ]	CNN	98.33
Ramakrishna et al. [ 41 ]	EfficientNet	98.00
Method	Ensemble System	Acc. (%)

TABLE VIII : Performance comparison of the proposed method with existing approaches on the Kaggle dataset.

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Improving patient outcomes depends on the prompt and accurate diagnosis of brain tumors, but manual MRI scan analysis is still time-consuming and unreliable. Although deep learning has shown promise, many of the models that are now in use are computationally intensive and have difficulty handling the intrinsic complexity and variety of different types of brain tumors. In this work, we propose a lightweight yet high-performing Convolutional Neural Network (CNN) for multi-class brain tumor classification, employing MRI images to target gliomas, meningiomas, pituitary tumors, and healthy (no tumor) instances. The model was rigorously evaluated on two publicly accessible datasets from Figshare and Kaggle. Leveraging efficient feature extraction and optimized training strategies, our CNN achieved classification accuracies of 99.03% and 99.28%, along with ROC scores of 99.88% and 99.94% on Dataset 1 and Dataset 2, respectively-all while utilizing significantly fewer parameters than popular pre-trained architectures. In contrast to cutting-edge models like DenseNet201, MobileNetV2, VGG19, Xception, InceptionV3, and ResNet50, our approach consistently demonstrated superior performance with reduced computational overhead. These findings highlight the potential of the proposed model as a practical and reliable diagnostic aid in clinical environments.

Abstract

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