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首页> 外文期刊>BMC Medical Informatics and Decision Making >Multi-label classification of fundus images based on graph convolutional network
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Multi-label classification of fundus images based on graph convolutional network

机译：基于图形卷积网络的基底图像的多标签分类

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Diabetic Retinopathy (DR) is the most common and serious microvascular complication in the diabetic population. Using computer-aided diagnosis from the fundus images has become a method of detecting retinal diseases, but the detection of multiple lesions is still a difficult point in current research. This study proposed a multi-label classification method based on the graph convolutional network (GCN), so as to detect 8 types of fundus lesions in color fundus images. We collected 7459 fundus images (1887 left eyes, 1966 right eyes) from 2282 patients (1283 women, 999 men), and labeled 8 types of lesions, laser scars, drusen, cup disc ratio ( $$C/D0.6$$ ), hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates. We constructed a specialized corpus of the related fundus lesions. A multi-label classification algorithm for fundus images was proposed based on the corpus, and the collected data were trained. The average overall F1 Score (OF1) and the average per-class F1 Score (CF1) of the model were 0.808 and 0.792 respectively. The area under the ROC curve (AUC) of our proposed model reached 0.986, 0.954, 0.946, 0.957, 0.952, 0.889, 0.937 and 0.926 for detecting laser scars, drusen, cup disc ratio, hemorrhages, retinal arteriosclerosis, microaneurysms, hard exudates and soft exudates, respectively. Our results demonstrated that our proposed model can detect a variety of lesions in the color images of the fundus, which lays a foundation for assisting doctors in diagnosis and makes it possible to carry out rapid and efficient large-scale screening of fundus lesions.
机译：糖尿病视网膜病变（DR）是糖尿病群中最常见和最严重的微血管并发症。使用计算机辅助诊断来自眼底图像已成为检测视网膜疾病的方法，但多个病变的检测仍然是当前研究的难点。本研究提出了一种基于图形卷积网络（GCN）的多标签分类方法，以便在彩色眼底图像中检测8种类型的眼底病变。从2282名患者（1283名女性，999名男子）和标有8种病变，激光疤痕，杯盘比（0.6 $$; 0.6 $$ ），出血，视网膜动脉硬化，微瘤，硬渗透物和软渗出物。我们构建了相关的眼底病变的专门语料库。基于语料库提出了一种基底图像的多标签分类算法，训练收集的数据。模型的平均总体F1得分（OF1）和平均每级F1分数（CF1）分别为0.808和0.792。我们提出的模型的ROC曲线（AUC）下的区域达到0.986,0.954,0.946,0.957,0.952,0.889,0.937和0.926，用于检测激光疤痕，司令，杯盘比，出血，视网膜动脉硬化，微安瘤，硬渗出物软渗出物分别。我们的结果表明，我们所提出的模型可以检测眼底的彩色图像中的各种病变，这为协助医生进行诊断，并使得可以进行快速高效的大规模筛查的基础病变。

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