Scholarlink

Using a range of deep learning models and machine learning methods, this article attempts to categorize the severity levels of diabetic retinopathy. The algorithm first preprocesses the dataset, which consists of retinal pictures classified into various degrees of severity, to extract significant characteristics from the photos, it makes use of transfer learning techniques and pre-trained deep learning models like ResNet50, VGG16, and ResNet18. For classification jobs, it also uses machine learning techniques like extreme learning machine (ELM) and support vector machine (SVM). The best-performing models are saved for later use after the program assesses each model and algorithm's performance on the dataset.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

An Intelligent Approach to Analyze Severity Levels of Diabetic Retinopathy by Data Classification Using Transfer Learning

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Kapil Chaturvedi,
  • Vijay Bhandari,
  • Anil Kothari,
  • Adem Tekerek,
  • Rishabh Tiwari,
  • Ritu Shrivastava

摘要

Using a range of deep learning models and machine learning methods, this article attempts to categorize the severity levels of diabetic retinopathy. The algorithm first preprocesses the dataset, which consists of retinal pictures classified into various degrees of severity, to extract significant characteristics from the photos, it makes use of transfer learning techniques and pre-trained deep learning models like ResNet50, VGG16, and ResNet18. For classification jobs, it also uses machine learning techniques like extreme learning machine (ELM) and support vector machine (SVM). The best-performing models are saved for later use after the program assesses each model and algorithm's performance on the dataset.