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The creation of a few-shot learning system for the diagnosis of diabetic retinopathy is the focus of this project. As per the National Eye Institute (NEI) report from 2021, diabetic retinopathy is a primary cause of vision loss in adults aged 20–74. Therefore, it is imperative to recognize and treat the condition at an early stage. NEI is a resource-based knowledge hub about conditions and eye health. The main goal of this project is to use the few-shot learning technique to improve the capacity for recognizing diabetic retinopathy in its early stages. By enhancing data optimization, the suggested model can reduce the quantity of data handling by localizing the necessary discriminative regions and working with a selective amount of data. In the given inputs, it also enables the model to recognize and follow unique and novel patterns. This model will also function with the recommendations study concerning patient barriers related to geography and cost. In the medical and healthcare industries, diabetic retinopathy detection is applied. This enables us to forecast therapy outcomes, manage progression risk, and lessen both the acute and long-term effects.

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A Quick and Acceptable Eye Check Diabscan: Small-Shoot Magnificence in Retinopathy Diagnosis

  • 会议论文

  • 发表日期：

    2025年

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  • P. Pavankumar,
  • Mohd. Abdul Naqi,
  • K. Sravan Abhilash,
  • J. Seetaram

摘要

The creation of a few-shot learning system for the diagnosis of diabetic retinopathy is the focus of this project. As per the National Eye Institute (NEI) report from 2021, diabetic retinopathy is a primary cause of vision loss in adults aged 20–74. Therefore, it is imperative to recognize and treat the condition at an early stage. NEI is a resource-based knowledge hub about conditions and eye health. The main goal of this project is to use the few-shot learning technique to improve the capacity for recognizing diabetic retinopathy in its early stages. By enhancing data optimization, the suggested model can reduce the quantity of data handling by localizing the necessary discriminative regions and working with a selective amount of data. In the given inputs, it also enables the model to recognize and follow unique and novel patterns. This model will also function with the recommendations study concerning patient barriers related to geography and cost. In the medical and healthcare industries, diabetic retinopathy detection is applied. This enables us to forecast therapy outcomes, manage progression risk, and lessen both the acute and long-term effects.