TR-IncepResNet: A Template Registration Guided Inception-ResNet Framework for Robust Brain Tumor Detection

Abstract

Accurate brain tumor categorization by magnetic resonance imaging (MRI) is essential for early identification and therapy planning. Traditional deep learning algorithms might be challenging to utilize in clinical settings due to varying patient anatomy and interpretability issues. This study presents a new approach to brain tumor classification that combines Template Registration (TR) preprocessing and a customized TR-IncepResNet architecture. Template registration reduces spatial variance in MRI data, enabling the model to concentrate on tumor-specific properties. The proposed model uses Inception modules for multi-scale feature extraction and residual connections, resulting in consistent training and performance. Tested on a balanced dataset of 10,000 enhanced MRI images from four classes (glioma, meningioma, pituitary tumor, and no tumor), the proposed technique achieves 99.6% accuracy and an AUC of 1.000, exceeding ResNet-based baselines. Grad-CAM++ is also used to improve interpretability by creating visual heatmaps that corroborate the model’s emphasis on clinically significant tumor locations. These findings validate TR-IncepResNet as a cutting-edge, interpretable, and therapeutically promising approach for automated brain tumor classification.

• Brain Tumor Classification
• Template Registration
• Inception-ResNet
• Explainable AI (XAI)
• Grad-CAM++