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All Journal International Journal of Engineering Continuity
Budi Santoso, Irwan
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Civil Engineering, Building, Construction & Architecture Computer Science & IT Electrical & Electronics Engineering Engineering Materials Science & Nanotechnology Mechanical Engineering

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Ensemble Combination of CNN for MRI-Based Brain Tumor Classification Sidqi, Akbar; Budi Santoso, Irwan; Harini, Sri
International Journal of Engineering Continuity Vol. 4 No. 1 (2025): ijec
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/ijec.v4i1.357

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.
Co-Authors Sidqi, Akbar Sri Harini