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Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model

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Abstract

Diabetes is considered to be the foundation of a slew of other health issues and late consequences, according to medical experts. The rise in diabetes-related illnesses has presented a challenge to the healthcare industry. Diabetic neuropathy, diabetic nephropathy, and diabetic retinopathy are just a few complications that can arise from diabetes. Diabetic Retinopathy is characterized by red lesions, bright lesions, and neovascularization. Bright lesions (exudates) are the second clinically visible lesions that appear after red lesions. The present challenge in DR detection is to make early diagnosis of DR disease more accessible by minimizing the cost and personnel requirements while preserving or enhancing DR detection quality. The challenge can be solved by using automated or computer-assisted DR detection in retinal images. The precise location and shape of blood vessels and the optic disc play critical roles in accurately diagnosing and classifying dark and bright lesions for the early detection of DR. The primary aim of the proposed model is to create an automated model for identifying bright lesions for non-proliferative stage diabetic retinopathy screening using deep learning architecture. It presents algorithms for removing the background of the images, eliminating the optic disc (OD), and the segmentation of candidate lesions. The model was trained and evaluated using MESSIDOR and e-ophtha Ex public datasets and obtained maximum accuracy, sensitivity, specificity, and F1-score of 97.54%, 90.34%, 98.24%, 93.28% and 96.32%, 95.73%, 97.12%, 96.74% respectively.

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Acknowledgements

The authors would like to thank the SRM Institute of Science and Technology, Department of Computing Technologies  for providing an excellent atmosphere for researching on this topic.

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Authors and Affiliations

  1. Department of Computing Technologies, School of Computing, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chennai, 603203, India

    P. Saranya & K. M. Umamaheswari

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  1. P. Saranya
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  2. K. M. Umamaheswari
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Correspondence to P. Saranya.

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Saranya, P., Umamaheswari, K.M. Detection of exudates from retinal images for non-proliferative diabetic retinopathy detection using deep learning model. Multimed Tools Appl 83, 52253–52273 (2024). https://doi.org/10.1007/s11042-023-17462-8

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  • DOI: https://doi.org/10.1007/s11042-023-17462-8

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