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• W3048139637 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." @default.
• W3048139637 created "2020-08-13" @default.
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• W3048139637 date "2021-01-01" @default.
• W3048139637 modified "2023-09-24" @default.
• W3048139637 title "A Survey on Prematurity Detection of Diabetic Retinopathy Based on Fundus Images Using Deep Learning Techniques" @default.
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• W3048139637 doi "https://doi.org/10.4018/978-1-7998-5071-7.ch006" @default.
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