Early Detection of Diabetic Retinopathy using Machine Learning

  • Unique Paper ID: 164905
  • Volume: 10
  • Issue: 12
  • PageNo: 2812-2816
  • Abstract:
  • Diabetes is a widespread condition that can cause significant side effects, such as Diabetic Retinopathy (DR), which is damage to the blood vessels in the retina. This disease is a major cause of vision loss in advanced nations, where there is a considerable risk of blindness. By 2030, it is predicted that 44% of the world's population would have diabetes, affecting an estimated 366 million people globally. Traditionally, qualified medical professionals have used manual screening of color fundus pictures to diagnose DR. However this approach takes a lot of time and is prone to human mistake, especially when handling a multitude of diabetes patient photos. A rising number of people are interested in using machine learning (ML) techniques for automated screening as a solution to these problems. Automated machine learning-based screening has the potential to optimize the detection process, lessen the workload for medical practitioners, and enhance diagnosis precision and consistency. The aim of this study is to develop a robust system that can precisely analyze retinal images for early indicators of diabetic retinopathy by utilizing machine learning techniques. This would enable swift treatment and medical care. The technology attempts to improve the precision of identifying various phases of retinopathy by utilizing cutting-edge image processing algorithms, giving clinicians important information for patient care. In the end, our strategy seeks to reduce the variation in diagnoses based on human variables, guaranteeing accurate and consistent detection of diabetic retinopathy.

Cite This Article

  • ISSN: 2349-6002
  • Volume: 10
  • Issue: 12
  • PageNo: 2812-2816

Early Detection of Diabetic Retinopathy using Machine Learning

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