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Detection of Diabetic Retinopathy Using Custom CNN to Segment the Lesions

Saleh Albahli1,2,*, Ghulam Nabi Ahmad Hassan Yar3

1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
2 Department of Computer Science, Kent State University, Kent, OH, USA
3 Department of Electrical and Computer Engineering, Air University, Islamabad, Pakistan

* Corresponding Author: Saleh Albahli. Email: email

Intelligent Automation & Soft Computing 2022, 33(2), 837-853. https://doi.org/10.32604/iasc.2022.024427

Abstract

Diabetic retinopathy is an eye deficiency that affects the retina as a result of the patient having Diabetes Mellitus caused by high sugar levels. This condition causes the blood vessels that nourish the retina to swell and become distorted and eventually become blocked. In recent times, images have played a vital role in using convolutional neural networks to automatically detect medical conditions, retinopathy takes this to another level because there is need not for just a system that could determine is a patient has retinopathy, but also a system that could tell the severity of the procession and if it would eventually lead to macular edema. In this paper, we designed three deep learning models that would detect the severity of diabetic retinopathy from images of the retina and also determine if it would lead to macular edema. Since our dataset was a small one, we employed three techniques for generating images from the ones we have, the techniques are Brightness, color and, contrast (BCC) enhancing, Color jitters (CJ), and Contrast Limited Adaptive Histogram Equalization (CLAHE). After the dataset was ready, we used it to train the ResNet50, VGG16, and VGG19 models both for determining the severity of the retinopathy and also the chances of macular edema. After validation, the models yielded very reasonable results.

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APA Style
Albahli, S., Yar, G.N.A.H. (2022). Detection of diabetic retinopathy using custom CNN to segment the lesions. Intelligent Automation & Soft Computing, 33(2), 837-853. https://doi.org/10.32604/iasc.2022.024427
Vancouver Style
Albahli S, Yar GNAH. Detection of diabetic retinopathy using custom CNN to segment the lesions. Intell Automat Soft Comput . 2022;33(2):837-853 https://doi.org/10.32604/iasc.2022.024427
IEEE Style
S. Albahli and G.N.A.H. Yar, "Detection of Diabetic Retinopathy Using Custom CNN to Segment the Lesions," Intell. Automat. Soft Comput. , vol. 33, no. 2, pp. 837-853. 2022. https://doi.org/10.32604/iasc.2022.024427



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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