Deep Learning for Brain Tumour Analysis: A Systematic Review of CNN-Transformer Hybrids in Multimodal Imaging.

Background: Brain tumour detection and analysis using medical imaging requires the extraction of both local spatial features and global contextual representations. Although convolutional neural networks (CNNs) excel at capturing local spatial patterns and Transformer-based architectures model long-range dependencies effectively, the optimal architectural paradigm for clinical deployment remains unresolved. This systematic review and meta-analysis evaluates hybrid CNN-Transformer architectures for brain tumour detection, focusing on the integration of local and global feature learning, diagnostic accuracy and computational efficiency. The roles of generative adversarial networks (GANs) for addressing data scarcity and multimodal imaging fusion for diagnostic completeness are also critically examined. Methods: A systematic search was conducted across IEEE Xplore, PubMed, Scopus and Google Scholar for studies published between January 2021 and May 2025. From 1876 initially identified articles, 94 met the prespecified inclusion criteria following quality assessment using the QUADAS-2 and ROBINS-I frameworks. A random-effects meta-analysis of diagnostic accuracy was performed using the DerSimonian-Laird estimator, with statistical heterogeneity quantified using I Results: Across all 94 included studies, the pooled diagnostic accuracy was 93.5% (95% CI: 92.7%-94.4%); however, confirmed publication bias (Egger's Conclusions: Descriptive comparison of mean observed accuracies based on study counts is insufficient for confirmatory meta-analysis, suggesting hybrid CNN-Transformer architectures may offer diagnostic accuracy advantages over CNN- and Transformer-only approaches; this observation is hypothesis-generating only and requires validation in a larger, more balanced evidence base. Among integration strategies, parallel architectures demonstrated the most favourable accuracy efficiency balance in the reviewed evidence. GANs and multimodal imaging function as essential architectural enablers, addressing data scarcity and diagnostic incompleteness, respectively. Significant challenges remain in computational efficiency, noise robustness and generalisation to rare tumour subtypes, representing priority directions for future research.