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ORIGINAL RESEARCH article

Front. Neurosci.
Sec. Visual Neuroscience
Volume 18 - 2024 | doi: 10.3389/fnins.2024.1471089
This article is part of the Research Topic Advances in Computer Vision: From Deep Learning Models to Practical Applications View all 8 articles

SMLS-YOLO: An Extremely Lightweight Pathological Myopia Instance Segmentation Method

Provisionally accepted
Hanfei Xie Hanfei Xie Baoxi Yuan Baoxi Yuan *Chengyu Hu Chengyu Hu Yujie Gao Yujie Gao Feng Wang Feng Wang Yuqian Wang Yuqian Wang Chunlan Wang Chunlan Wang Peng Chu Peng Chu
  • Xijing University, Xi'an, China

The final, formatted version of the article will be published soon.

    Pathological myopia is a major cause of blindness among people under 50 years old and can result in severe vision loss in extreme cases. Currently, its detection primarily relies on manual methods, which are slow and heavily dependent on the expertise of physicians, making them impractical for large-scale screening. To tackle these challenges, we propose SMLS-YOLO, an instance segmentation method based on YOLOv8n-seg. Designed for efficiency in large-scale screenings, SMLS-YOLO employs an extremely lightweight model. First, StarNet is introduced as the backbone of SMLS-YOLO to extract image features. Subsequently, the StarBlock from StarNet is utilized to enhance the C2f, resulting in the creation of the C2f-Star feature extraction module. Furthermore, shared convolution and scale reduction strategies are employed to optimize the segmentation head for a more lightweight design. Lastly, the model incorporates the Multi-Head Self-Attention (MHSA) mechanism following the backbone to further refine the feature extraction process. Experimental results on the pathological myopia dataset demonstrate that SMLS-YOLO outperforms the baseline YOLOv8n-seg by reducing model parameters by 46.9%, increasing Box

    Keywords: Pathological myopia, SMLS-YOLO, Instance segmentation, Lightweigh, Image feature extraction

    Received: 26 Jul 2024; Accepted: 09 Sep 2024.

    Copyright: © 2024 Xie, Yuan, Hu, Gao, Wang, Wang, Wang and Chu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Baoxi Yuan, Xijing University, Xi'an, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.