Developments in the detection of diabetic retinopathy: a state-of-the-art review of computer-aided diagnosis and machine learning methods

Abstract

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.

Supplementary information: The online version contains supplementary material available at 10.1007/s10462-022-10185-6.

Keywords: Computer-aided diagnosis; Diabetic retinopathy; Fuzzy logic; Fuzzy sets; Image processing; Machine learning; Retina; Retinopathy.

Fig. 1

The author co-citation network showing all the authors who had made publications in the field of automated DR detection since 2015

Fig. 2

An overview of the main contents of this article, presented as a tree diagram for visualization

Fig. 3

Proportions among all the 84 entries among the New Fundus Algorithms that belong to each of the 10 model groups

Fig. 4

The model groups of the 84 entries among the New Fundus Algorithms, arranged by their year of publication

Fig. 5

The year of publications of the 84 entries among the New Fundus Algorithms, arranged by the model groups they belong

Fig. 6

An Euler diagram showing the distribution of all the 84 entries of the New Fundus Algorithm according to their constituent models and method

Fig. 7

The essential structure of a typical CNN architecture for general DR grading

Fig. 8

The implementation of preprocessing of fundus image in prior of feeding into CNN

Fig. 9

The implementation of preprocessing among the entries of the New Fundus Algorithm built using pure CNN

Fig. 10

A representation of the direct adaptation of a single RGP-CNN for general DR grading

Fig. 11

The algorithm developed by Zeng et al. (2019) for General DR Detection using two mutually identical CNN architecture

Fig. 12

The CNN architecture developed by Shankar et al. (2020a) for general DR grading

Fig. 13

An Euler diagram showing the distribution of all the 84 entries of the new fundus algorithm according to their designed tasks