Hybrid Deep Learning Models in Image Classification: Integrating CNNs with Attention, Capsule Networks, and Graph Neural Networks

Image classification has been transformed by convolutional neural networks (CNNs), yet single-architecture solutions are increasingly reaching performance plateaus on complex, fine-grained, and cross-domain tasks. A new research frontier therefore explores hybrid deep learning models that fuse complementary architectural paradigms—attention mechanisms, Capsule Networks, recurrent/transformer layers, and graph neural networks (GNNs)—with CNN backbones to capture richer spatial hierarchies, relational cues, and long-range dependencies. This review synthesizes 2020-2025 literature on such hybrids, with a focus on models that (i) insert channel- or self-attention modules into CNN feature pipelines; (ii) replace late fully connected layers with Capsule Networks to exploit part–whole relationships; (iii) append GNN layers to reason over pixel-region graphs; and (iv) orchestrate multi-branch designs combining several of the above. We analyse 60+ primary studies, benchmarking gains on ImageNet, CIFAR, hyperspectral, medical, and remote-sensing datasets. Hybrid schemes commonly deliver 2-8 % accuracy improvements and enhanced robustness to occlusion and viewpoint change. Nevertheless, they incur higher FLOPs, memory footprints, and hyper-parameter complexity. A critical contribution of this review is a taxonomy (Figure 2) and a consolidated performance table (Table 1) that links architectural choices to empirical gains. We discuss optimisation strategies (knowledge distillation, sparse attention, lightweight graph convolutions) and examine open challenges: cross-domain generalisation, explainability, and sustainable energy budgets. Finally, we outline future directions—neuro-symbolic fusion, federated hybrid learning, and automated architecture search—to guide the next wave of research.