LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis

• URL: http://arxiv.org/abs/2412.05968v1
• Date: Sun, 08 Dec 2024 15:21:37 GMT
• Title: LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image Analysis
• Authors: Mehwish Mehmood, Shahzaib Iqbal, Tariq Mahmood Khan, Ivor Spence, Muhammad Fahim,
• Abstract summary: This paper presents a novel lightweight encoder-decoder model that segments retinal vessels to improve the efficiency of disease detection. It incorporates multi-scale convolutional blocks in the encoder to accurately identify vessels of various sizes and thicknesses. It outperforms existing models with dice scores of 86.44%, 84.22%, and 87.88%, respectively.
• Score: 1.347667211255822
• License:
• Abstract: The analysis of retinal images for the diagnosis of various diseases is one of the emerging areas of research. Recently, the research direction has been inclined towards investigating several changes in retinal blood vessels in subjects with many neurological disorders, including dementia. This research focuses on detecting diseases early by improving the performance of models for segmentation of retinal vessels with fewer parameters, which reduces computational costs and supports faster processing. This paper presents a novel lightweight encoder-decoder model that segments retinal vessels to improve the efficiency of disease detection. It incorporates multi-scale convolutional blocks in the encoder to accurately identify vessels of various sizes and thicknesses. The bottleneck of the model integrates the Focal Modulation Attention and Spatial Feature Refinement Blocks to refine and enhance essential features for efficient segmentation. The decoder upsamples features and integrates them with the corresponding feature in the encoder using skip connections and the spatial feature refinement block at every upsampling stage to enhance feature representation at various scales. The estimated computation complexity of our proposed model is around 29.60 GFLOP with 0.71 million parameters and 2.74 MB of memory size, and it is evaluated using public datasets, that is, DRIVE, CHASE\_DB, and STARE. It outperforms existing models with dice scores of 86.44\%, 84.22\%, and 87.88\%, respectively.

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