Combining Artificial Intelligence and Image Processing for Diagnosing Diabetic Retinopathy in Retinal Fundus Images

Authors

  • Obaida M. Al-hazaimeh Al-Balqa Applied University
  • Ashraf Abu-Ein
  • Nedal Tahat The Hashemite University
  • Ma’moun Al-Smadi Al-Balqa Applied University
  • Malek Al-Nawashi Al-Balqa Applied University

DOI:

https://doi.org/10.3991/ijoe.v18i13.33985

Keywords:

diabetic retinopathy, machine learning, deep learning, Fundus images, ophthalmology, object detection, DCNN, object classification

Abstract


Retinopathy is an eye disease caused by diabetes, and early detection and treatment can potentially reduce the risk of blindness in diabetic retinopathy sufferers. Using retinal Fundus images, diabetic retinopathy can be diagnosed, recognized, and treated. In the current state of the art, sensitivity and specificity are lacking. However, there are still a number of problems to be solved in state-of-the-art techniques like performance, accuracy, and being able to identify DR disease effectively with greater accuracy. In this paper, we have developed a new approach based on a combination of image processing and artificial intelligence that will meet the performance criteria for the detection of disease-causing diabetes retinopathy in Fundus images. Automatic detection of diabetic retinopathy has been proposed and has been carried out in several stages. The analysis was carried out in MATLAB using software-based simulation, and the results were then compared with those of expert ophthalmologists to verify their accuracy. Different types of diabetic retinopathy are represented in the experimental evaluation, including exudates, micro-aneurysms, and retinal hemorrhages. The detection accuracies shown by the experiments are greater than 98.80 percent.

Covr iJOE Vol. 18 No. 13 (2022)

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Published

2022-10-19

How to Cite

Al-hazaimeh, O. M., Abu-Ein, A. . ., Tahat, N. ., Al-Smadi, M. ., & Al-Nawashi, M. (2022). Combining Artificial Intelligence and Image Processing for Diagnosing Diabetic Retinopathy in Retinal Fundus Images. International Journal of Online and Biomedical Engineering (iJOE), 18(13), pp. 131–151. https://doi.org/10.3991/ijoe.v18i13.33985

Issue

Vol. 18 No. 13 (2022)

Section

Papers

License

Copyright (c) 2022 Obaida M. Al-hazaimeh, Ashraf Abu-Ein, Nedal Tahat, Malek Al-Nawashi, Ma’moun Al-Smadi

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