Hybrid Subcutaneous Vascular Structure Detection Networks

In the field of medical image analysis, particularly in subcutaneous vascular imaging, one of the main challenges is accurately distinguishing between veins, arteries, and capillaries within complex biological structures. These vascular networks exhibit intricate and overlapping patterns, making it difficult for traditional image recognition methods to correctly classify the different vessel types. Convolutional neural networks (CNNs) have shown great promise in a variety of visual recognition tasks, including biomedical imaging. However, their performance tends to plateau when confronted with the complexities of vascular images, as they often struggle to capture long-range dependencies and fine-grained structural details, which are essential for accurately identifying and differentiating vascular structures. To address this, we propose an enhanced CNN architecture that focuses on extracting high-level features at multiple granularities and effectively integrating these features through multi-path dependencies. This method leverages a novel convolutional operator that shifts the focus from large convolutions to more compact, efficient kernels while maintaining the capacity for long-range feature fusion. Our approach demonstrates superior performance in subcutaneous vascular image recognition compared to existing methods, including standard CNNs and transformer-based models, highlighting its ability to identify vascular structures with greater precision. Through our experimental analysis, we show that smaller convolution kernels, such as $3 \times 3$, can efficiently replace larger ones without compromising the accuracy of vascular feature detection, offering a promising direction for future research in the field.