PURPOSE To automatically detect and classify the lesions of diabetic retinopathy (DR) in fundus fluorescein angiography (FFA) images using deep learning algorithm through comparing 3 convolutional neural networks (CNNs). METHODS A total of 4067 FFA images from Eye Center at the Second Affiliated Hospital of Zhejiang University School of Medicine were annotated with 4 kinds of lesions of DR, including non-perfusion regions (NP), microaneurysms, leakages, and laser scars. Three CNNs including DenseNet, ResNet50, and VGG16 were trained to achieve multi-label classification, which means the algorithms could identify 4 retinal lesions above at the same time. RESULTS The area under the curve (AUC) of DenseNet reached 0.8703, 0.9435, 0.9647, and 0.9653 for detecting NP, microaneurysms, leakages, and laser scars, respectively. For ResNet50, AUC was 0.8140 for NP, 0.9097 for microaneurysms, 0.9585 for leakages, and 0.9115 for laser scars. And for VGG16, AUC was 0.7125 for NP, 0.5569 for microaneurysms, 0.9177 for leakages, and 0.8537 for laser scars. CONCLUSIONS Experimental results demonstrate that DenseNet is a suitable model to automatically detect and distinguish retinal lesions in the FFA images with multi-label classification, which lies the foundation of automatic analysis for FFA images and comprehensive diagnosis and treatment decision-making for DR.

中文翻译：

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糖尿病视网膜病变视网膜病变的多标签分类，可基于深度学习自动分析眼底荧光素血管造影。

目的通过比较3个卷积神经网络（CNN），使用深度学习算法自动检测和分类眼底荧光素血管造影（FFA）图像中的糖尿病性视网膜病变（DR）病变。方法对来自浙江大学医学院附属第二医院眼科中心的4067张FFA图像进行注解，包括4种DR病变，包括非灌注区域（NP），微动脉瘤，渗漏和激光疤痕。训练了包括DenseNet，ResNet50和VGG16在内的三个CNN，以实现多标签分类，这意味着该算法可以同时识别上述4个视网膜病变。结果DenseNet的曲线下面积（AUC）分别达到NP，微动脉瘤，渗漏和激光疤痕检测的0.8703、0.9435、0.9647和0.9653。对于ResNet50，AUC为0。NP为8140，微动脉瘤为0.9097，泄漏为0.9585，激光疤痕为0.9115。对于VGG16，NP的AUC为0.7125，微动脉瘤的AUC为0.5569，渗漏的AUC为0.9177，激光疤痕的AUC为0.8537。结论实验结果表明，DenseNet是一种适用于自动检测和区分多标签分类的FFA图像中视网膜病变的模型，这为FFA图像的自动分析以及DR的综合诊断和治疗决策奠定了基础。