Abstract
Diabetic retinopathy is a disorder induced by long-term diabetes that can result in total blindness if not addressed. As a result, early detection of diabetic retinopathy is critical, as is the medical treatment to prevent its adverse effects. Manual ophthalmologist detection takes longer and produces considerable discomfort during examination. Machine learning has recently become one of the most popular strategies for improving performance in a variety of sectors, including medical picture analysis and classification. As a result, an automated system aids in the early detection of diabetic retinopathy. Using a combination of neural networks, this research offers the extraction of exudates, haemorrhages, and micro-aneurysms and classification by machine learning.
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