Selvachandran Ganeshsree, Quek Shio Gai, Paramesran Raveendran, Ding Weiping, Son Le Hoang
Department of Actuarial Science and Applied Statistics, Faculty of Business & Management, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia.
Institute of Computer Science and Digital Innovation, UCSI University, Jalan Menara Gading, Cheras, 56000 Kuala Lumpur, Malaysia.
Artif Intell Rev. 2023;56(2):915-964. doi: 10.1007/s10462-022-10185-6. Epub 2022 Apr 26.
The exponential increase in the number of diabetics around the world has led to an equally large increase in the number of diabetic retinopathy (DR) cases which is one of the major complications caused by diabetes. Left unattended, DR worsens the vision and would lead to partial or complete blindness. As the number of diabetics continue to increase exponentially in the coming years, the number of qualified ophthalmologists need to increase in tandem in order to meet the demand for screening of the growing number of diabetic patients. This makes it pertinent to develop ways to automate the detection process of DR. A computer aided diagnosis system has the potential to significantly reduce the burden currently placed on the ophthalmologists. Hence, this review paper is presented with the aim of summarizing, classifying, and analyzing all the recent development on automated DR detection using fundus images from 2015 up to this date. Such work offers an unprecedentedly thorough review of all the recent works on DR, which will potentially increase the understanding of all the recent studies on automated DR detection, particularly on those that deploys machine learning algorithms. Firstly, in this paper, a comprehensive state-of-the-art review of the methods that have been introduced in the detection of DR is presented, with a focus on machine learning models such as convolutional neural networks (CNN) and artificial neural networks (ANN) and various hybrid models. Each AI will then be classified according to its type (e.g. CNN, ANN, SVM), its specific task(s) in performing DR detection. In particular, the models that deploy CNN will be further analyzed and classified according to some important properties of the respective CNN architectures of each model. A total of 150 research articles related to the aforementioned areas that were published in the recent 5 years have been utilized in this review to provide a comprehensive overview of the latest developments in the detection of DR.
The online version contains supplementary material available at 10.1007/s10462-022-10185-6.
全球糖尿病患者数量呈指数级增长，导致糖尿病视网膜病变（DR）病例数同样大幅增加，DR是糖尿病引发的主要并发症之一。若不加以治疗，DR会使视力恶化并导致部分或完全失明。由于未来几年糖尿病患者数量将继续呈指数级增长，为满足对日益增多的糖尿病患者进行筛查的需求，合格眼科医生的数量也需要相应增加。这使得开发DR检测自动化方法变得至关重要。计算机辅助诊断系统有潜力显著减轻目前眼科医生所承受的负担。因此，本文献综述旨在总结、分类和分析自2015年至今利用眼底图像进行DR自动检测的所有最新进展。此类工作对所有关于DR的最新研究进行了前所未有的全面综述，这可能会增进对DR自动检测所有最新研究的理解，特别是对那些部署机器学习算法的研究。首先，本文对DR检测中已引入的方法进行了全面的最新技术综述，重点关注机器学习模型，如卷积神经网络（CNN）和人工神经网络（ANN）以及各种混合模型。然后，每个人工智能将根据其类型（如CNN、ANN、SVM）及其在执行DR检测中的特定任务进行分类。特别是，部署CNN的模型将根据每个模型各自的CNN架构的一些重要属性进一步分析和分类。本综述利用了最近5年发表的与上述领域相关的150篇研究文章，以全面概述DR检测的最新进展。
在线版本包含可在10.1007/s10462-022-10185-6获取的补充材料。
