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Individuals with diabetes face the risk of developing Proliferative Diabetic Retinopathy (PDR) which is a retinal disorder that leads to neovascularization. Multiple studies propose image processing for neovascularization detection, but its unpredictable growth and small size still pose challenges. Deep learning techniques with automatic feature extraction are gaining prevalence in neovascularization detection. The proposed work introduces a method based on transfer learning. Two distinct models are constructed using transfer learning, one is utilizing MobileNet and the other is combining GoogLeNet and ResNet18. An alternative method involves pre-trained CNN for feature extraction followed by SVM classification. A comparison was conducted across all methods in terms of accuracy. MobileNet achieves the highest accuracy of 97.6%.

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An Efficient Neovascularization Detection in Fundus Images Using Transfer Learning

  • 会议论文

  • 发表日期：

    2026年

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  • D. V. Lalita Parameswari,
  • R. Pallavi Reddy,
  • Aakifah Fatima

摘要

Individuals with diabetes face the risk of developing Proliferative Diabetic Retinopathy (PDR) which is a retinal disorder that leads to neovascularization. Multiple studies propose image processing for neovascularization detection, but its unpredictable growth and small size still pose challenges. Deep learning techniques with automatic feature extraction are gaining prevalence in neovascularization detection. The proposed work introduces a method based on transfer learning. Two distinct models are constructed using transfer learning, one is utilizing MobileNet and the other is combining GoogLeNet and ResNet18. An alternative method involves pre-trained CNN for feature extraction followed by SVM classification. A comparison was conducted across all methods in terms of accuracy. MobileNet achieves the highest accuracy of 97.6%.