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In this study, we propose LDMRes-Net, a lightweight dual-multiscale residualblock-based computational neural network tailored for medical imagesegmentation on IoT and edge platforms. Conventional U-Net-based models facechallenges in meeting the speed and efficiency demands of real-time clinicalapplications, such as disease monitoring, radiation therapy, and image-guidedsurgery. LDMRes-Net overcomes these limitations with its remarkably low numberof learnable parameters (0.072M), making it highly suitable forresource-constrained devices. The model's key innovation lies in its dualmulti-residual block architecture, which enables the extraction of refinedfeatures on multiple scales, enhancing overall segmentation performance. Tofurther optimize efficiency, the number of filters is carefully selected toprevent overlap, reduce training time, and improve computational efficiency.The study includes comprehensive evaluations, focusing on segmentation of theretinal image of vessels and hard exudates crucial for the diagnosis andtreatment of ophthalmology. The results demonstrate the robustness,generalizability, and high segmentation accuracy of LDMRes-Net, positioning itas an efficient tool for accurate and rapid medical image segmentation indiverse clinical applications, particularly on IoT and edge platforms. Suchadvances hold significant promise for improving healthcare outcomes andenabling real-time medical image analysis in resource-limited settings.

LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms