Optimized Weighted Voting System for Brain Tumor Classification Using MRI Images
This work addresses the problem of accurate brain tumor classification for medical diagnosis, but it is incremental as it builds on existing ensemble methods with specific optimizations.
The paper tackled brain tumor classification from MRI images by proposing a weighted ensemble learning approach that combines deep learning and traditional machine learning models, achieving state-of-the-art accuracy on the Figshare and Kaggle datasets.
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.