LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and Edge Platforms
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 40 of its 89 references that are in the map. First posted on arXiv 9 Jun 2023.
Builds on
40 of 89 references.
References · 40
- 2015Deep Residual Learning for Image Recognitioncited by 51,809
- 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
- 2014Fully Convolutional Networks for Semantic Segmentationcited by 4,845
- 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,827
- 2016DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFscited by 3,239
- 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,320
- 2016V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentationcited by 2,284
- 2019Searching for MobileNetV3cited by 1,984
- 2021Vision Transformers for Dense Predictioncited by 1,789
- 2021TransUNet: Transformers Make Strong Encoders for Medical Image Segmentationcited by 1,552
- 2018UNet++: A Nested U-Net Architecture for Medical Image Segmentationcited by 1,518
- 2015SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentationcited by 1,483
28 more
- 2018Attention U-Net: Learning Where to Look for the Pancreascited by 1,420
- 2020Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformerscited by 1,368
- 2017ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devicescited by 1,194
- 2018ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Designcited by 1,132
- 2021Segmenter: Transformer for Semantic Segmentationcited by 855
- 2019UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentationcited by 583
- 2016RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentationcited by 463
- 2021Medical Transformer: Gated Axial-Attention for Medical Image Segmentationcited by 319
- 2020BiSeNet V2: Bilateral Network with Guided Aggregation for Real-time Semantic Segmentationcited by 294
- 2017ICNet for Real-Time Semantic Segmentation on High-Resolution Imagescited by 261
- 2019CE-Net: Context Encoder Network for 2D Medical Image Segmentationcited by 219
- 2019MultiResUNet : Rethinking the U-Net Architecture for Multimodal Biomedical Image Segmentationcited by 195
- 2015ParseNet: Looking Wider to See Bettercited by 176
- 2022SegViT: Semantic Segmentation with Plain Vision Transformerscited by 105
- 2018DUNet: A deformable network for retinal vessel segmentationcited by 89
- 2019Bi-Directional ConvLSTM U-Net with Densley Connected Convolutionscited by 82
- 2018Light-Weight RefineNet for Real-Time Semantic Segmentationcited by 27
- 2019RAUNet: Residual Attention U-Net for Semantic Segmentation of Cataract Surgical Instrumentscited by 27
- 2020BiO-Net: Learning Recurrent Bi-directional Connections for Encoder-Decoder Architecturecited by 25
- 2023Retinal Vessel Segmentation via a Multi-resolution Contextual Network and Adversarial Learningcited by 19
- 2022MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentationcited by 13
- 2021RC-Net: A Convolutional Neural Network for Retinal Vessel Segmentationcited by 12
- 2020Transfer Learning Through Weighted Loss Function and Group Normalization for Vessel Segmentation from Retinal Imagescited by 8
- 2022Orientation and Context Entangled Network for Retinal Vessel Segmentationcited by 8
- 2019SegNAS3D: Network Architecture Search with Derivative-Free Global Optimization for 3D Image Segmentationcited by 7
- 2022SuperVessel: Segmenting High-resolution Vessel from Low-resolution Retinal Imagecited by 6
- 2018M2U-Net: Effective and Efficient Retinal Vessel Segmentation for Resource-Constrained Environmentscited by 5
- 2022Neural Network Compression by Joint Sparsity Promotion and Redundancy Reductioncited by 5
Built on by
Citing papers per year: 2023: 1.
1 paper.