Hierarchical Attention Lightweight U-Net for Gastro-Intestinal Tract Segmentation

Accurate and efficient segmentation of gastrointestinal (GI) tract images play a pivotal role in medical diagnosis. However, conventional deep learning models, such as U-Net, often incur high computational costs and memory overheads, hindering their deployment in resource-constrained clinical settings. This article proposes a novel hierarchical attention lightweight U-Net (HALU-Net) architecture designed specifically to address these limitations. HALU-Net incorporates a computationally efficient U-Net backbone, optimized through techniques like depth-wise separable convolutions and a hierarchical attention mechanism. While depth-wise separable convolution helps to reduce the model parameters, the hierarchical attention enable the model to selectively focus on both localized, fine-grained details and global contextual information within GI images at every decoding stage. This multi-level attention approach is essential for accurate segmentation of complex and variable GI tract structures. The HALU-Net model is rigorously evaluated on the publicly available UW-Madison GI tract image segmentation dataset. Results demonstrate that HALU-Net achieves competitive segmentation performance as measured by metrics such as dice coefficient and Jaccard index while boasting a substantially reduced parameter count compared to prevailing U-Net architectures. This significant decrease in computational complexity and memory requirements positions HALU-Net as a promising solution for facilitating accurate medical diagnoses of GI conditions, even in computation-constrained environments.