深度学习在糖尿病视网膜病变分级中的应用

Application of Deep Learning in Grading of Diabetic Retinopathy

近年来,糖尿病视网膜病变(diabetic retinopathy,DR)成为全球失明人口增加的主要原因,早期的DR严重程度分级对防止DR患者视力丧失尤为重要.由于糖尿病患者数量的逐年上升,DR分级的需求量也不断增加,然而传统的人工分级不能满足日益增长的需求,且人工分级耗时费力.深度学习技术的发展,为DR检测和分级提供了高效率且更可靠的手段.虽然,目前的DR二元检测已经取得十分好的效果,然而由于糖尿病视网膜病变的复杂性和病变程度之间的差距细微,DR严重程度分级仍然是一个具有挑战性的问题.本文对近年来涌现的DR分级方法进行了研究和总结:介绍了基于VGG、InceptionNet、ResNet、EfficientNet、DenseNet、CapsNet模型的6种深度学习分级方法;并介绍了基于多网络融合的DR分级方法;最后对基于深度学习的DR分级方法的研究趋势进行总结和展望.

In recent years,diabetic retinopathy(DR)has become the main reason for the global blind population increase.The early DR severity classification is particularly important to prevent vision loss in DR patients.As the number of diabetes patients grows year by year,the demand for DR grading is also rising.However,the traditional manual grading cannot meet the growing demands,and it is time-consuming and laborious.The development of deep learning technology provides a more efficient and reliable means for DR detection and grading.Although the current DR binary detection has yielded good results,DR severity grading is still challenging due to the slight differences between DR complexity and lesion degree.This work studies and summarizes DR grading methods in recent years.It introduces six deep learning classification methods based on VGG,InceptionNet,ResNet,EfficientNet,DenseNet,and CapsNet models.In addition,the study presents DR grading methods based on multi-network fusion.Finally,summary and prospect are provided for the research trends of DR grading methods based on deep learning.