智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (3): 259-267.doi: 10.11959/j.issn.2096-6652.202127

• 专刊:目标智能检测与识别 • 上一篇    

基于深度卷积集成网络的视网膜多种疾病筛查和识别方法

王禾扬, 杨启鸣, 朱旗   

  1. 南京航空航天大学计算机科学与技术学院,江苏 南京 211100
  • 修回日期:2021-07-08 出版日期:2021-09-01 发布日期:2021-09-01
  • 作者简介:王禾扬(1996− ),男,南京航空航天大学计算机科学与技术学院硕士生,主要研究方向为模式识别、医学影像处理
    杨启鸣(1996− ),男,南京航空航天大学计算机科学与技术学院硕士生,主要研究方向为模式识别、医学影像处理
    朱旗(1985− ),男,博士,南京航空航天大学计算机科学与技术学院副教授,主要研究方向为模式识别、医学影像分析、生物特征识别
  • 基金资助:
    中央高校基本科研业务费专项资金项目(NT2020024)

Retinal multi-disease screening and recognition method based on deep convolution ensemble network

Heyang WANG, Qiming YANG, Qi ZHU   

  1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211100, China
  • Revised:2021-07-08 Online:2021-09-01 Published:2021-09-01
  • Supported by:
    Fundamental Research Funds for the Central Universities(NT2020024)

摘要:

针对视网膜疾病种类繁多、病灶位置不固定等特点,提出一种基于深度卷积集成网络的视网膜多种疾病筛查和识别方法。首先,根据视网膜眼底图像裁剪掉两侧黑色边框,并去除图像中的噪声,以降低对眼底图像的干扰,提高图像的清晰度;之后,通过对处理完成的视网膜眼底图像使用裁剪、旋转等数据增强方法来扩增数据集;再建立基于深度卷积神经网络的模型进行特征提取,并在网络模型微调后完成视网膜疾病筛查和识别任务,最终将多个模型的结果进行集成。实验结果表明,该方法针对视网膜疾病的筛查和识别的问题取得了较好的效果,视网膜疾病筛查的准确率达到96.05%,视网膜疾病识别的准确率达到72.55%。

关键词: 视网膜眼底图像, 疾病筛查, 疾病识别, 深度卷积网络, 集成模型

Abstract:

As for the characteristics of various types of retinal diseases and uncertainty of the location of the lesions, a retinal multi-disease screening and recognition method based on deep convolutional ensemble network was proposed.Firstly, the black borders on both sides of the retinal fundus image were cut off, and the noise in the image was removed to reduce the interference to the retinal image and increase the clarity of the image.After that, data augmentation methods such as cropping and rotating were performed to process retinal fundus image to amplify the dataset.Then, a model based on deep convolutional neural network was built for feature extraction, and the network model was fine-tuned to complete the task of screening and identifying retinal diseases.Finally, the results of multiple models were ensembled.The experimental results show that this method has achieved good results for the screening and recognition of retinal diseases, the accuracy of retinal disease screening is 96.05%, and the accuracy of retinal disease recognition is 72.55%.

Key words: retinal fundus image, disease screening, disease recognition, deep convolutional network, ensemble model

中图分类号: 

No Suggested Reading articles found!