Abstract
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.
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Wang, X., Qiu, Y., Chen, X., Wu, J., Zou, Q., Mu, N. (2023). Decision Tree Fusion and Improved Fundus Image Classification Algorithm. In: Yu, C., Zhou, J., Song, X., Lu, Z. (eds) Green, Pervasive, and Cloud Computing. GPC 2022. Lecture Notes in Computer Science, vol 13744. Springer, Cham. https://doi.org/10.1007/978-3-031-26118-3_3
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