Hybrid Deep Learning Architectures for MRI-Based Brain Tumor Diagnosis

Aarti Verma , Prof. Ashish Tiwari

Deep Learning, MRI, Brain Tumor, Convolutional Neural Networks, T1, T2, FLAIR and GAN

Hybrid deep learning architectures have emerged as a powerful solution for addressing the complexity of MRI-based brain tumor diagnosis, a domain characterized by heterogeneous tumor morphology, multimodal imaging protocols, and the need for accurate, early detection. Traditional convolutional neural networks (CNNs), although highly effective at learning spatial features, often struggle to capture long-range dependencies, multi-sequence relationships, and contextual variations inherent in brain tumor MRI data. By integrating complementary modules such as recurrent neural networks, transformers, generative adversarial networks, and classical machine-learning classifiers, hybrid models leverage the strengths of each component to enhance feature representation, robustness, and generalization across scanners and institutions. Advances in multimodal MRI--including T1, T2, FLAIR, and contrast-enhanced sequences--further motivate hybrid architectures by providing diverse inputs that benefit from sophisticated fusion strategies. Preprocessing pipelines involving skull stripping, normalization, and denoising underpin the reliability of these systems, while hybrid CNN-GAN and CNN-Transformer approaches demonstrate improvements in tumor detection, classification, and segmentation, particularly for complex tasks such as delineating glioma subregions or distinguishing tumor grades. Benchmarking on datasets such as BraTS, TCGA, and REMBRANDT reveals consistent performance gains for hybrid models, although issues of domain shift, annotation scarcity, and limited cross-dataset validation remain significant barriers. Despite impressive accuracy metrics--including enhanced Dice scores, F1-scores, and reduced false positives--clinical integration is hindered by computational cost, interpretability challenges, and regulatory constraints. Overall, hybrid deep learning architectures represent a promising path toward more reliable and clinically useful MRI-based brain tumor diagnosis, while highlighting the need for improved datasets, standardized evaluation protocols, and greater emphasis on robustness and transparency.

Volume 13 Issue 11, November-2025

Pages : e922-e932