In this study, we propose LDMRes-Net, a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms. Conventional U-Net-based models face challenges in meeting the speed and efficiency demands of real-time clinical applications, such as disease monitoring, radiation therapy, and image-guided surgery. LDMRes-Net overcomes these limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices. The model's key innovation lies in its dual multi-residual block architecture, which enables the extraction of refined features on multiple scales, enhancing overall segmentation performance. To further optimize efficiency, the number of filters is carefully selected to prevent overlap, reduce training time, and improve computational efficiency. The study includes comprehensive evaluations, focusing on segmentation of the retinal image of vessels and hard exudates crucial for the diagnosis and treatment of ophthalmology. The results demonstrate the robustness, generalizability, and high segmentation accuracy of LDMRes-Net, positioning it as an efficient tool for accurate and rapid medical image segmentation in diverse clinical applications, particularly on IoT and edge platforms. Such advances hold significant promise for improving healthcare outcomes and enabling real-time medical image analysis in resource-limited settings.

翻译：本研究提出LDMRes-Net——一种基于轻量级双多尺度残差块的计算神经网络，专为物联网与边缘平台上的医学图像分割设计。传统基于U-Net的模型难以满足疾病监测、放射治疗和图像引导手术等实时临床应用对速度与效率的需求。LDMRes-Net凭借其极低的可学习参数量（0.072M）突破这些限制，高度适配资源受限设备。该模型的核心创新在于双多残差块架构，能够从多尺度提取精炼特征，提升整体分割性能。为进一步优化效率，滤波器数量经过精心筛选以避免重叠、缩短训练时间并提高计算效率。研究进行了全面评估，重点针对眼科诊断与治疗中关键的视网膜血管和硬性渗出物分割。结果表明，LDMRes-Net具有鲁棒性、泛化能力和高分割精度，可作为多样化临床应用中（尤其在物联网与边缘平台上）实现精准快速医学图像分割的高效工具。这些进展对于改善医疗保健效果、在资源有限环境下实现实时医学图像分析具有重要前景。