Adaptive Feature Integration in CNN–Transformer Networks for Efficient and Interpretable Visual Classification
Over the past few years, deep learning has changed substantially following the emergence of Transformer architectures, which are particularly effective for representing long-range dependencies that are difficult for conventional Convolutional Neural Networks (CNNs). Whereas CNNs are well suited to extracting local spatial features using convolutional operations, Transformers are effective at representing global context through self-attention. Hybrid CNN–Transformer architectures have been developed to combine the respective strengths of the two approaches. A limitation of many existing models is their reliance on static or manually designed fusion strategies, which can restrict adaptability, add computational cost, and make the resulting decisions harder to interpret. The present study develops a novel adaptive fusion framework that adaptively combines CNN and Transformer features through learnable gating, attention-based feature integration, and explainable-AI methods. The resulting framework is intended to improve both computational efficiency and model interpretability, thereby addressing important limitations of current hybrid designs. The experimental evaluation uses benchmark datasets such as ImageNet, CIFAR-100, and medical imaging datasets. The reported results show that the proposed model performs better than the comparison architectures with respect to accuracy, efficiency, and interpretability.