Hybrid CNN-Transformer Architectures for Efficient Image Segmentation and Object Recognition

Deep learning has revolutionized image segmentation and object recognition tasks by replacing manual feature extraction with automated, data-driven techniques. Convolutional Neural Networks (CNNs) have emerged as the dominant architecture for extracting spatially localized features due to their ability to leverage locality and weight-sharing properties. Despite their success, CNNs often struggle to capture long-range dependencies, which are crucial for accurately segmenting complex structures in high-dimensional data such as 3D medical images. This limitation motivates the integration of complementary architectures that can model global relationships effectively. In this work, we propose a hybrid CNNTransformer framework designed to enhance both image segmentation and object recognition performance. The CNN component is responsible for generating robust, hierarchical feature representations, while the Transformer module leverages self-attention mechanisms to capture long-range dependencies across the entire feature map. By combining local feature extraction with global context modeling, the proposed approach achieves superior accuracy and efficiency compared to conventional CNN-based methods. Experimental results demonstrate that this architecture delivers significant improvements in segmentation precision and object recognition robustness, making it well-suited for real-world medical imaging and computer vision applications.