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Detection of Diabetic Retinopathy using Convolutional Neural Networks to prevent Vision-Threatening Diseases

  • Unique Paper ID: 179599
  • Volume: 11
  • Issue: 12
  • PageNo: 7026-7031
  • Abstract:
  • Diabetic retinopathy (DR), a common complication of diabetes, can lead to severe vision loss if left untreated. Early detection is crucial for timely intervention and improved patient outcome.The propo sed method leverages deep learning techniques, specifically CNNs, to analyze retinal fundus images for signs of diabetic retinopathy. The CNN model was trained on a large dataset of labeled retinal images and learned to identify subtle features associated with different stages of the disease. The system aims to classify images into multiple categories ranging from no retinopathy to severe cases requiring immediate medical attention. The key advantages of this approach include its potential for high accuracy, scalability, and ability to assist healthcare professionals in efficiently screening large populations. The system can be integrated into existing healthcare workflows, providing rapid and consistent results to support clinical decision making. Preliminary results showed promising performance in detecting various stages of diabetic retinopathy, with high sensitivity and specificity. Future work will focus on further improving the model's accuracy, expanding the dataset to include diverse populations, and conducting clinical validation studies to assess the real-world effectiveness of the system in preventing vision- threatening complications of diabetes.
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  • ISSN: 2349-6002
  • Volume: 11
  • Issue: 12
  • PageNo: 7026-7031

Detection of Diabetic Retinopathy using Convolutional Neural Networks to prevent Vision-Threatening Diseases

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