MRI-based brain tumor ensemble classification using two stage score level fusion and CNN models

This paper proposes a novel two-stage approach to improve brain tumor classification accuracy using the Br35H MRI Scan Dataset. The first stage employs advanced image enhancement algorithms, GFPGAN and Real-ESRGAN, to enhance the image dataset’s quality, sharpness, and resolution. Nine deep learning models are then trained and tested on the enhanced dataset, experimenting with five optimizers. In the second stage, ensemble learning algorithms like weighted sum, fuzzy rank, and majority vote are used to combine the scores from the trained models, enhancing prediction results. The top 2, 3, 4, and 5 classifiers are selected for ensemble learning at each rating level. The system’s performance is evaluated using accuracy, recall, precision, and F1-score. It achieves 100% accuracy when using the GFPGAN-enhanced dataset and combining the top 5 classifiers through ensemble learning, outperforming current methodologies in brain tumor classification. These compelling results underscore the potential of our approach in providing highly accurate and effective brain tumor classification. • Novel two-stage approach using image enhancement and ensemble learning for brain tumor classification. • GFPGAN and Real-ESRGAN algorithms improve MRI image quality and resolution in Stage 1. • Nine CNN models trained with five optimizers on original and enhanced Br35H MRI datasets. • Ensemble learning methods integrate predictions from top-performing models for better accuracy. • Achieves 100% accuracy using GFPGAN enhancement and top-5 model ensemble configuration.