Automatic classification of retinopathy with attention ConvNeXt

Wenbo HUANG; Yuxiang HUANG; Yuan YAO; Yang YAN

doi:10.37188/OPE.20223017.2147

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Automatic classification of retinopathy with attention ConvNeXt

Information Sciences | 更新时间：2022-10-08

- Automatic classification of retinopathy with attention ConvNeXt
- Optics and Precision Engineering Vol. 30, Issue 17, Pages: 2147-2154(2022)
- 作者机构：
  
  1.长春师范大学计算机科学与技术学院，吉林长春 130032
  2.中国科学院重大任务局，北京 100864
- 作者简介：
- 基金信息：
- DOI：10.37188/OPE.20223017.2147
  CLC： TP391.7
- Received：31 May 2022，
  
  Revised：17 June 2022，
  
  Published：10 September 2022
- 稿件说明：
移动端阅览
黄文博,黄钰翔,姚远等.融合注意力的ConvNeXt视网膜病变自动分级[J].光学精密工程,2022,30(17):2147-2154.

HUANG Wenbo,HUANG Yuxiang,YAO Yuan,et al.Automatic classification of retinopathy with attention ConvNeXt[J].Optics and Precision Engineering,2022,30(17):2147-2154.
黄文博,黄钰翔,姚远等.融合注意力的ConvNeXt视网膜病变自动分级[J].光学精密工程,2022,30(17):2147-2154. DOI： 10.37188/OPE.20223017.2147.

HUANG Wenbo,HUANG Yuxiang,YAO Yuan,et al.Automatic classification of retinopathy with attention ConvNeXt[J].Optics and Precision Engineering,2022,30(17):2147-2154. DOI： 10.37188/OPE.20223017.2147.

摘要

由于视网膜病变的类间图像特征差别小及分类临界值相对模糊，自动分级算法存在识别与分级准确率低的问题，提出了融合高效通道注意力（Efficient Channel Attention， ECA）特征的ConvNeXt视网膜病变自动分级模型。针对数据集中数据不足的问题，采用水平翻转左右变换的方法扩充数据，并引入相关数据集来均衡数据的分布。针对眼底图像中出现的图像模糊、光照不均等问题，采用Graham方法对图像进行预处理突出病变特征。提出了融合注意力的ConvNeXt网络来辅助医生诊断视网膜病变，引入ECA机制，并设计了E-Block模块，该模块具有高性能、低参数的特性，能够在训练过程中有效捕捉跨通道交互的信息，同时避免降维。采用迁移学习方法训练网络的所有层参数，加入dropout方法避免ConvNeXt网络的学习能力过强导致的过拟合问题。实验结果表明，所提出的模型敏感性为95.20%，特异度为98.80%，准确率为95.21%。与常用的网络相比，本文方法针对视网膜病变自动分级各项性能指标均有提高。

Abstract

Due to the small differences in image features between classes and the relative fuzzy classification threshold of retinopathy， automatic classification algorithms are challenged by problems related to low recognition and classification accuracy. This paper proposes an automatic classification model for retinopathy based on an improved ConvNeXt network. Aiming at solving the problem of insufficient data in the data set， the horizontal flip left and right transformation method is used to expand the data， and related data sets are introduced to balance data distribution. To solve problems related to image blurring and uneven illumination in the fundus image， the Graham method was used to predict the image. The characteristics of the lesions are also highlighted. In this paper， an attention-fused ConvNeXt network was proposed to assist doctors in diagnosing retinopathy， an efficient channel attention mechanism was introduced， and an E-Block module was designed to channel interaction information while avoiding dimensionality reduction. The transfer learning method was used to train all layer parameters of the network， and the dropout method was added to avoid the overfitting problem caused by the strong learning ability of the ConvNeXt network. The results show that the sensitivity， specificity， and accuracy of the proposed model are 95.20%， 98.80%， and 95.21%， respectively. Compared with the ConvNeXt and other networks， the performance indexes of this network model for automatic classification of retinopathy.

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references

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