Ensemble Combination of CNN for MRI-Based Brain Tumor Classification
Year: 2025
Type: article
Abstract: Classifying 17 types of brain tumors remains a major challenge in the medical field, especially in improving diagnostic accuracy and accelerating patient care. This study proposes a CNN-based model with an ensemble combination approach to improve accuracy by integrating multiple architectures through Majority Voting and Weighted Average for more reliable predictions. The models are evaluated using accuracy, precision, recall, and F1-score metrics. The results show that CNN3 with Nadam achieves the best performance (accuracy: 0.90–0.91), outperforming CNN1 (0.87–0.89) and CNN2 (0.82–0.87). The ensemble combination improves accuracy across all models, with CNN3 achieving the highest accuracy (0.96), followed by CNN1 (0.94–0.95) and CNN2 (0.91–0.92). This study demonstrates that the ensemble combination approach can improve the performance of brain tumor classification using deep learning, contributing to faster and more accurate medical diagnosis. Furthermore, these findings open up opportunities for further research in advancing brain tumor detection systems.
Authors: Akbar Sidqi, Irwan Budi Santoso, Sri Harini
Institution: Universitas Islam Negeri Maulana Malik Ibrahim
Language: English
Subfield: Neurology
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4408868332