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Background <p>Myopia is a major cause of vision impairment. To improve the efficiency of myopia screening, this paper proposes a deep learning model, <i>X</i>-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.</p> Approach <p>First, fundus images are enhanced and preprocessed to improve feature extraction effectiveness and enhance the model’s generalization capability. Then, the model is trained using fivefold cross-validation, leveraging dynamic convolution and depthwise separable convolution to extract features from each fundus image and classify the severity of myopia. Next, Grad-CAM is employed to visualize the model’s decision-making process, highlighting the regions contributing to classification. Finally, a user-friendly GUI interface is developed to intuitively present the classification results, thereby enhancing the system’s usability and practical applicability.</p> Results <p>The experimental results show that the proposed method achieves an accuracy of 0.9104, a precision of 0.8154, a recall of 0.8177, an F1-score of 0.8147, and a specificity of 0.9376 in the classification of myopia severity.</p> Significance <p>The model significantly outperforms existing conventional deep learning models in terms of accuracy, demonstrating strong effectiveness and reliability.</p>

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Deep learning for predicting myopia severity classification method

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年7月9日

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  • WangMeiYu Xing,
  • XiaoNa Li,
  • JingShu Ni,
  • YuanZhi Zhang,
  • ZhongSheng Li,
  • Yong Liu,
  • YiKun Wang,
  • Yao Huang

摘要

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.

Approach

First, fundus images are enhanced and preprocessed to improve feature extraction effectiveness and enhance the model’s generalization capability. Then, the model is trained using fivefold cross-validation, leveraging dynamic convolution and depthwise separable convolution to extract features from each fundus image and classify the severity of myopia. Next, Grad-CAM is employed to visualize the model’s decision-making process, highlighting the regions contributing to classification. Finally, a user-friendly GUI interface is developed to intuitively present the classification results, thereby enhancing the system’s usability and practical applicability.

Results

The experimental results show that the proposed method achieves an accuracy of 0.9104, a precision of 0.8154, a recall of 0.8177, an F1-score of 0.8147, and a specificity of 0.9376 in the classification of myopia severity.

Significance

The model significantly outperforms existing conventional deep learning models in terms of accuracy, demonstrating strong effectiveness and reliability.