Advanced dynamic ensemble framework with explainability driven insights for precision brain tumor classification across datasets.
Authors
Affiliations (6)
Affiliations (6)
- Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, India.
- Department of Computing, Universiti Teknologi PETRONAS, Seri Iskandar 32610, Malaysia.
- Positive Computing Research Center, Emerging & Digital Technologies Institute, Universiti Teknologi PETRONAS, Seri Iskandar, Malaysia.
- Department of Computer Science, Applied College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.
- School of Computing, Gachon University, Seongnam-si, 13120, Republic of Korea. [email protected].
- Department of Electrical Power, Adama Science and Technology University, Adama, 1888, Ethiopia. [email protected].
Abstract
Accurate detection of brain tumors remains a significant challenge due to the diversity of tumor types along with human interventions during diagnostic process. This study proposes a novel ensemble deep learning system for accurate brain tumor classification using MRI data. The proposed system integrates fine-tuned Convolutional Neural Network (CNN), ResNet-50 and EfficientNet-B5 to create a dynamic ensemble framework that addresses existing challenges. An adaptive dynamic weight distribution strategy is employed during training to optimize the contribution of each networks in the framework. To address class imbalance and improve model generalization, a customized weighted cross-entropy loss function is incorporated. The model obtains improved interpretability through explainabile artificial intelligence (XAI) techniques, including Grad-CAM, SHAP, SmoothGrad, and LIME, providing deeper insights into prediction rationale. The proposed system achieves a classification accuracy of 99.4% on the test set, 99.48% on the validation set, and 99.31% in cross-dataset validation. Furthermore, entropy-based uncertainty analysis quantifies prediction confidence, yielding an average entropy of 0.3093 and effectively identifying uncertain predictions to mitigate diagnostic errors. Overall, the proposed framework demonstrates high accuracy, robustness, and interpretability, highlighting its potential for integration into automated brain tumor diagnosis systems.