Deep learning for predicting myopia severity classification method.

Journal: Biomedical engineering online
Published Date: Jul 9, 2025

Abstract

BACKGROUND: Myopia is a major cause of vision impairment. To improve the efficiency of myopia screening, this paper proposes a deep learning model, X-ENet, which combines the advantages of depthwise separable convolution and dynamic convolution to classify different severities of myopia. The proposed model not only enables precise extraction of detailed features from fundus images but also achieves lightweight processing, thereby improving both computational efficiency and classification accuracy.

Authors

  • WangMeiYu Xing
    College of Biomedical Engineering, Anhui Medical University, Hefei, 230011, China.
  • Xiaona Li
    Department of Radiology, Third Hospital of Hebei Medical University, Shijiangzhuang, China.
  • JingShu Ni
    Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei, 230031, China.
  • Yuanzhi Zhang
    Inner Mongolia Medical University, Department of Orthopedics, The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, North Street, Inner Mongolia, 010050, China. Electronic address: dryzzhang@163.com.
  • ZhongSheng Li
    Anhui Institute of Optics and Fine Mechanics, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis Treatment Technology and Instrument, Hefei, 230031, China.
  • Yong Liu
    Department of Critical care medicine, Shenzhen Hospital, Southern Medical University, Guangdong, Shenzhen, China.
  • Yikun Wang
    Key Laboratory of Gravitational Wave Precision Measurement of Zhejiang Province, School of Physics and Photoelectric Engineering, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 310024, China.
  • Yao Huang
    Department of Digestive Diseases, Huashan Hospital, Fudan University, Shanghai 200040, P.R. China.

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