Decision Tree Fusion and Improved Fundus Image Classification Algorithm

被引：0
|
作者：
Wang, Xiaofang [1 ]
Qiu, Yanhua [2 ]
Chen, Xin [2 ]
Wu, Jialing [2 ]
Zou, Qianying [1 ]
Mu, Nan [3 ]
机构：
[1] Geely Univ China, Chengdu 641423, Sichuan, Peoples R China
[2] Chengdu Coll Univ Elect Sci & Technol China, Chengdu 611731, Sichuan, Peoples R China
[3] Sichuan Normal Univ, Chengdu 610066, Sichuan, Peoples R China
来源：
GREEN, PERVASIVE, AND CLOUD COMPUTING, GPC 2022 | 2023年 / 13744卷
关键词：
Image classification; Butterworth parameter function; Improved UNet plus; Residual attention mechanism; Decision tree;
D O I：
10.1007/978-3-031-26118-3_3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In order to improve the effect of glaucoma fundus image classification, a new algorithm based on decision tree and UNet++ was proposed. Firstly, the image is divided into three channels of RGB, and the extracted green channel image is enhanced with the Butterworth parameter function of the fusion power function. Then the improved UNet++ network model is used to extract the texture features of the fundus image, and the residual module is used to enhance the texture features. The results of the experiment show that the average accuracy, the average specificity and the average sensitivity of the improved algorithm increase by 9.2%, 6.4% and 6.5% respectively. The improved algorithm is effective in glaucoma fundus image classification.
引用
收藏
页码：35 / 49
页数：15
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