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A Survey on Prematurity Detection of Diabetic Retinopathy Based on Fundus Images Using Deep Learning Techniques

A Survey on Prematurity Detection of Diabetic Retinopathy Based on Fundus Images Using Deep Learning Techniques
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Author(s): Amiya Kumar Dash (KIIT University, India) and Puspanjali Mohapatra (International Institute of Information Technology, Bhubaneswar, India)
Copyright: 2021
Pages: 16
Source title: Deep Learning Applications in Medical Imaging
Source Author(s)/Editor(s): Sanjay Saxena (International Institute of Information Technology, India) and Sudip Paul (North-Eastern Hill University, India)
DOI: 10.4018/978-1-7998-5071-7.ch006

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Abstract

Diabetic retinopathy (DR) is a disease related to eye correlated with long-standing diabetes. It is a leading cause of blindness among working adults. Detection of this condition in the early stage is critical for good prognosis. Present day detection of DR normally requires digital fundus image or images generated using optical coherence tomography (OCT). As OCT are high-priced, diagnosis of DR using fundus image will benefit for the patient and the ophthalmologists. Manual inspection of morphological changes in blood vessels, microaneurysms, exudates, hemorrhages, and macula are time consuming and tedious tasks. So, designing a computer-aided system helps in analyzing the morphological changes and identifying the DR. This chapter reviews the applications of machine learning and deep learning algorithms for detection of nonproliferative diabetic retinopathy by analyzing fundus images.

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