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. 2022 Feb 24;22(5):1803.
doi: 10.3390/s22051803.

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Muhammad Shoaib Farooq  1 , Ansif Arooj  2 , Roobaea Alroobaea  3 , Abdullah M Baqasah  4 , Mohamed Yaseen Jabarulla  5 , Dilbag Singh  5 , Ruhama Sardar  1
Affiliations

Untangling Computer-Aided Diagnostic System for Screening Diabetic Retinopathy Based on Deep Learning Techniques

Muhammad Shoaib Farooq et al. Sensors (Basel). .

Abstract

Diabetic Retinopathy (DR) is a predominant cause of visual impairment and loss. Approximately 285 million worldwide population is affected with diabetes, and one-third of these patients have symptoms of DR. Specifically, it tends to affect the patients with 20 years or more with diabetes, but it can be reduced by early detection and proper treatment. Diagnosis of DR by using manual methods is a time-consuming and expensive task which involves trained ophthalmologists to observe and evaluate DR using digital fundus images of the retina. This study aims to systematically find and analyze high-quality research work for the diagnosis of DR using deep learning approaches. This research comprehends the DR grading, staging protocols and also presents the DR taxonomy. Furthermore, identifies, compares, and investigates the deep learning-based algorithms, techniques, and, methods for classifying DR stages. Various publicly available dataset used for deep learning have also been analyzed and dispensed for descriptive and empirical understanding for real-time DR applications. Our in-depth study shows that in the last few years there has been an increasing inclination towards deep learning approaches. 35% of the studies have used Convolutional Neural Networks (CNNs), 26% implemented the Ensemble CNN (ECNN) and, 13% Deep Neural Networks (DNN) are amongst the most used algorithms for the DR classification. Thus using the deep learning algorithms for DR diagnostics have future research potential for DR early detection and prevention based solution.

Keywords: automated detection; deep learning; deep neural network; diabetic retinopathy.

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Conflict of interest statement

There is no conflict of interest for this research.

Figures

Figure A1
Figure A1
The obtained results from the execution of the study filtering process.
Figure 1
Figure 1
Comparison between Normal and DR eye [2].
Figure 2
Figure 2
Lesions in retinal fundus image.
Figure 3
Figure 3
Systematic Literature Review Methodology.
Figure 4
Figure 4
Target combination of words to form Search String.
Figure 5
Figure 5
Studies Selection Process and Results.
Figure 6
Figure 6
Year-wise Publication Results.
Figure 7
Figure 7
Taxonomy of Diabetic Retinopathy (DR).
Figure 8
Figure 8
Five stages of Diabetic Retinopathy.
Figure 9
Figure 9
Five stages of Diabetic Retinopathy.
Figure 10
Figure 10
Deep Learning based Models used for DR Classification.
Figure 11
Figure 11
Dataset Size Over Time [87].
See this image and copyright information in PMC

References

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    1. Introduction to Diabetes and Diabetic Retinopathy. [(accessed on 14 December 2021)]. Available online: https://www.visionaware.org/info/your-eye-condition/diabetic-retinopathy/1.
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