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Method and system for classifying diabetic retina images based on deep learning

机译:基于深度学习对糖尿病视网膜图像进行分类的方法和系统

摘要

A method for classifying diabetic retina images based on deep learning includes: obtaining a fundus image; importing the same fundus image into a microhemangioma lesion recognition model, a hemorrhage lesion recognition model and an exudation lesion recognition model for recognition; extracting lesion feature information from the recognition results, and then using a trained support vector machine classifier to classify the extracted lesion feature information to obtain a classification result. The microhemangioma lesion recognition model is obtained by extracting a candidate microhemangioma lesion region in the fundus image and inputting it into a CNN model for training; the hemorrhage lesion recognition model and the exudation lesion recognition model are obtained by labeling a region in the fundus image as a hemorrhage lesion region and an exudation lesion region, and then inputting the result into an FCN model for training. A system for the same is also disclosed.
机译:一种基于深度学习的糖尿病视网膜图像进行分类的方法包括:获得眼底图像; 将相同的眼底图像进口到微藻瘤病变识别模型中,出血性病变识别模型和识别渗出性病变识别模型; 从识别结果中提取病变特征信息,然后使用训练的支持向量机分类器对提取的病变特征信息进行分类以获得分类结果。 通过在眼底图像中提取候选微藻瘤病变区并将其输入CNN模型进行培训来获得微藻瘤病灶识别模型; 通过将眼底图像中的区域标记为出血病变区和渗出病变区域,然后将结果标记为散热性病变区域,然后将结果标记为训练的FCN模型来获得出血病变识别模型和渗透病变识别模型。 还公开了一种用于该系统。

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