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

TL;DR

LDMRes-Net is a lightweight, efficient neural network designed for real-time medical image segmentation on resource-constrained IoT and edge devices, achieving high accuracy with minimal parameters.

Contribution

The paper introduces LDMRes-Net, a novel dual-multiscale residual network that significantly reduces model size while maintaining high segmentation performance for medical images.

Findings

Achieves high segmentation accuracy on retinal images.

Uses only 0.072 million parameters, suitable for IoT devices.

Demonstrates robustness and generalizability across datasets.

Abstract

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.