Shekar Shreya, Satpute Nitin, Gupta Aditya
College of Engineering Pune, Department of Electronics and Telecommunication Engineering, Pune, Maharashtra, India.
Aarhus University, Department of Electrical and Computer Engineering, Aarhus, Denmark.
J Med Imaging (Bellingham). 2021 Nov;8(6):060901. doi: 10.1117/1.JMI.8.6.060901. Epub 2021 Nov 29.
The purpose of our review paper is to examine many existing works of literature presenting the different methods utilized for diabetic retinopathy (DR) recognition employing deep learning (DL) and machine learning (ML) techniques, and also to address the difficulties faced in various datasets used by DR. DR is a progressive illness and may become a reason for vision loss. Early identification of DR lesions is, therefore, helpful and prevents damage to the retina. However, it is a complex job in view of the fact that it is symptomless earlier, and also ophthalmologists have been needed in traditional approaches. Recently, automated identification of DR-based studies has been stated based on image processing, ML, and DL. We analyze the recent literature and provide a comparative study that also includes the limitations of the literature and future work directions. A relative analysis among the databases used, performance metrics employed, and ML and DL techniques adopted recently in DR detection based on various DR features is presented. Our review paper discusses the methods employed in DR detection along with the technical and clinical challenges that are encountered, which is missing in existing reviews, as well as future scopes to assist researchers in the field of retinal imaging.
我们这篇综述论文的目的是审视众多现有文献，这些文献介绍了利用深度学习（DL）和机器学习（ML）技术进行糖尿病视网膜病变（DR）识别所采用的不同方法，同时探讨DR在各种数据集中所面临的困难。DR是一种渐进性疾病，可能会导致视力丧失。因此，早期识别DR病变是有帮助的，可防止视网膜受损。然而，鉴于其早期无症状，而且传统方法需要眼科医生，这是一项复杂的工作。最近，基于图像处理、ML和DL的DR自动识别研究已经出现。我们分析了近期的文献，并提供了一项比较研究，其中还包括文献的局限性和未来的工作方向。本文还对基于各种DR特征的DR检测中最近使用的数据库、采用的性能指标以及ML和DL技术进行了相关分析。我们的综述论文讨论了DR检测中所采用的方法以及遇到的技术和临床挑战，而现有综述中缺少这些内容，同时还探讨了未来的研究范围，以帮助视网膜成像领域的研究人员。
