LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and
Edge Platforms
- URL: http://arxiv.org/abs/2306.06145v2
- Date: Thu, 7 Sep 2023 12:56:49 GMT
- Title: LDMRes-Net: Enabling Efficient Medical Image Segmentation on IoT and
Edge Platforms
- Authors: Shahzaib Iqbal, Tariq M. Khan, Syed S. Naqvi, Muhammad Usman, and
Imran Razzak
- Abstract summary: We propose a lightweight dual-multiscale residual block-based computational neural network tailored for medical image segmentation on IoT and edge platforms.
LDMRes-Net overcomes limitations with its remarkably low number of learnable parameters (0.072M), making it highly suitable for resource-constrained devices.
- Score: 9.626726110488386
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
Related papers
- Residual Connection Networks in Medical Image Processing: Exploration of ResUnet++ Model Driven by Human Computer Interaction [0.4915744683251151]
This paper introduces ResUnet++, an advanced hybrid model combining ResNet and Unet++.
It is designed to improve tumour detection and localisation while fostering seamless interaction between clinicians and medical imaging systems.
By incorporating HCI principles, the model provides intuitive, real-time feedback, enabling clinicians to visualise and interact with tumour localisation results effectively.
arXiv Detail & Related papers (2024-12-30T04:57:26Z) - Efficient MedSAMs: Segment Anything in Medical Images on Laptop [69.28565867103542]
We organized the first international competition dedicated to promptable medical image segmentation.
The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline.
The best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption.
arXiv Detail & Related papers (2024-12-20T17:33:35Z) - MAPUNetR: A Hybrid Vision Transformer and U-Net Architecture for Efficient and Interpretable Medical Image Segmentation [0.0]
We introduce MAPUNetR, a novel architecture that synergizes the strengths of transformer models with the proven U-Net framework for medical image segmentation.
Our model addresses the resolution preservation challenge and incorporates attention maps highlighting segmented regions, increasing accuracy and interpretability.
Our experiments show that the model maintains stable performance and potential as a powerful tool for medical image segmentation in clinical practice.
arXiv Detail & Related papers (2024-10-29T16:52:57Z) - Med-TTT: Vision Test-Time Training model for Medical Image Segmentation [5.318153305245246]
We propose Med-TTT, a visual backbone network integrated with Test-Time Training layers.
The model achieves leading performance in terms of accuracy, sensitivity, and Dice coefficient.
arXiv Detail & Related papers (2024-10-03T14:29:46Z) - L-SFAN: Lightweight Spatially-focused Attention Network for Pain Behavior Detection [44.016805074560295]
Chronic Low Back Pain (CLBP) afflicts millions globally, significantly impacting individuals' well-being and imposing economic burdens on healthcare systems.
While artificial intelligence (AI) and deep learning offer promising avenues for analyzing pain-related behaviors to improve rehabilitation strategies, current models, including convolutional neural networks (CNNs), have limitations.
We introduce hbox EmoL-SFAN, a lightweight CNN architecture incorporating 2D filters designed to capture the spatial-temporal interplay of data from motion capture and surface electromyography sensors.
arXiv Detail & Related papers (2024-06-07T12:01:37Z) - Dual-scale Enhanced and Cross-generative Consistency Learning for Semi-supervised Medical Image Segmentation [49.57907601086494]
Medical image segmentation plays a crucial role in computer-aided diagnosis.
We propose a novel Dual-scale Enhanced and Cross-generative consistency learning framework for semi-supervised medical image (DEC-Seg)
arXiv Detail & Related papers (2023-12-26T12:56:31Z) - Augmentation is AUtO-Net: Augmentation-Driven Contrastive Multiview
Learning for Medical Image Segmentation [3.1002416427168304]
This thesis focuses on retinal blood vessel segmentation tasks.
It provides an extensive literature review of deep learning-based medical image segmentation approaches.
It proposes a novel efficient, simple multiview learning framework.
arXiv Detail & Related papers (2023-11-02T06:31:08Z) - LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based
CNN for Retinal Blood Vessel Segmentation [0.0]
Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images.
Deep learning has shown promise in medical image segmentation, but its reliance on repeated convolution and pooling operations can hinder the representation of edge information.
We propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters.
arXiv Detail & Related papers (2023-09-10T09:03:53Z) - LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical
Imaging via Second-order Graph Matching [59.01894976615714]
We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets.
We have collected approximately 1.3 million medical images from 55 publicly available datasets.
LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
arXiv Detail & Related papers (2023-06-20T22:21:34Z) - InDuDoNet+: A Model-Driven Interpretable Dual Domain Network for Metal
Artifact Reduction in CT Images [53.4351366246531]
We construct a novel interpretable dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded.
We analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.
arXiv Detail & Related papers (2021-12-23T15:52:37Z) - Searching for Efficient Architecture for Instrument Segmentation in
Robotic Surgery [58.63306322525082]
Most applications rely on accurate real-time segmentation of high-resolution surgical images.
We design a light-weight and highly-efficient deep residual architecture which is tuned to perform real-time inference of high-resolution images.
arXiv Detail & Related papers (2020-07-08T21:38:29Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.