基于IHBMO-RF的眼底硬性渗出物检测
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Detection of Hard Exudate in Fundus Based on IHBMO-RF
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    摘要:

    随着社会经济的发展, 糖尿病视网膜病变患者越来越多, 为了减少患者的致盲率, 早期诊断至关重要. 其中眼底硬性渗出物的检测是诊断的重要环节, 传统的检测方法受到主观因素的影响, 准确度不高且效率较低, 为了辅助医生进行检测, 本文提出了一种基于IHBMO-RF算法的眼底硬性渗出物的检测算法, 通过引入佳点集原理对蜂群进行初始化, 不但能保持蜂群的多样性, 而且还能加快蜂群的收敛速度, 解决了机器学习中面临的局部最优的困境. 在公开的眼底数据库DiaretDB1下进行实验显示, 本文使用的算法准确率达到了95.4%, 与现有研究对比, 取得了较好的效果, 对辅助检测眼底硬性渗出物具有一定的意义.

    Abstract:

    With the social and economic development, the number of patients with diabetic retinopathy is increasing, and thus early diagnosis is of great significance to reduce the incidence of blindness. The hard exudate detection in the fundus is an important part of the diagnosis, but the traditional detection method is influenced by subjective factors with low accuracy and efficiency. Therefore, this study proposes a hard exudate detection algorithm based on the IHBMO-RF algorithm to assist doctors with detection. Specifically, the swarm is initialized through the introduction of the principle of the good-point set, which can not only keep the diversity of the swarm but also accelerate the convergence speed of the swarm. In this way, the problem of local optimization can be solved in machine learning. Experiments are conducted on the public fundus database DiaretDB1, and the results show that the accuracy of the proposed method reaches 95.4%. Compared with algorithms in the existing studies, the proposed algorithm has achieved a better effect, which is of certain significance for the auxiliary hard exudate detection in the fundus.

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赵仕成,马力,张伟,陈颖,殷伟东.基于IHBMO-RF的眼底硬性渗出物检测.计算机系统应用,2022,31(6):259-264

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  • 收稿日期:2021-08-22
  • 最后修改日期:2021-09-26
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  • 在线发布日期: 2022-05-26
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