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- W4380521665 abstract "One of the most common and complicated forms of diabetes, diabetic retinopathy (DR) is now recognised as the major cause of blindness on a global scale. It is also one of the most commonly observed forms of the disease. The development of DR is associated with a degeneration of the blood vessels in the retina. Over the course of the past few years, numerous approaches to DR detection have been put forward as a result of the growing recognition that an early diagnosis is critical to effective treatment. However, research done in recent times has revealed a fact that deep learning-based CNN structures and Transfer Learning-based Medent’s are widely employed in DR detection because of their superior performance in the medical sector. This is a reality that has been brought to light as a result of recent developments. This article presents a study on automated ways that are used to diagnose diabetic retinopathy utilising image processing and disease classification techniques as a result of such improvements in Deep Learning methodology. These advancements have led to the writing of this article." @default.
- W4380521665 created "2023-06-14" @default.
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- W4380521665 date "2021-02-26" @default.
- W4380521665 modified "2023-10-16" @default.
- W4380521665 title "A Review on the Detection of Diabetic Retinopathy through the Use of Deep Learning" @default.
- W4380521665 doi "https://doi.org/10.17762/msea.v70i2.2464" @default.
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