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ETDACVO: Structural-Fidelity-Aware Evolutionary Co-Optimization for Robust and Explainable Brain Tumor MRI
Indrakumar Krishnamurthy1, Ravikumar Manjunath1, Mohammed A S Al-Mohamadi1
1Department of MCA and Computer Science, Jnanasahyadri, Kuvempu University, Shivamogga 577 451, India.
Biomedicines
|July 28, 2026
Summary
Enhanced Tasmanian Devil Anti-Conservative Variable Optimization (ETDACVO) improves deep learning for brain tumor MRI classification by stabilizing training and enhancing cross-domain robustness. This hybrid system achieves faster convergence and better accuracy, making medical image analysis more reliable.
Area of Science:
- Artificial Intelligence
- Medical Imaging
- Machine Learning
Background:
- Deep learning for medical image analysis faces challenges due to heterogeneous protocols, limited labeled data, and domain shifts.
- Brain tumor MRI classification is particularly difficult under these conditions.
Purpose of the Study:
- To introduce ETDACVO (Enhanced Tasmanian Devil Anti-Conservative Variable Optimization), a novel hybrid evolutionary system.
- To improve convergence stability and cross-domain robustness in brain tumor MRI classification.
Main Methods:
- ETDACVO integrates Tasmanian Devil Optimization (TDO), Anti-Conservative Variable Optimization (ACVO), and EWMA smoothing.
- It simultaneously optimizes augmentation policies and optimizer dynamics within a single evolutionary loop.
- A convergence-aware explainability mechanism (CA-EA-GradCAM) was developed for tumor localization.
Main Results:
- ETDACVO improved classification accuracy by 2.3-2.5% and converged 19-22 epochs faster than baselines (p < 1 × 10^-5).
- Cross-dataset experiments showed 92.8% performance retention, indicating strong domain-shift resilience.
- CA-EA-GradCAM generated interpretable, confidence-aware tumor localization maps.
Conclusions:
- ETDACVO offers a robust, efficient framework for deep learning in medical image analysis.
- Joint optimization of augmentation and optimizer dynamics enhances stability, robustness, and interpretability.
- This approach shows promise for reliable brain tumor MRI classification with heterogeneous data.