Early Detection of Diabetic Retinopathy with Segmentation Model U-Net

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@article{1705807,
      author = {Adeyinka Mayowa-Majaro},
      title = {Early Detection of Diabetic Retinopathy with Segmentation Model U-Net},
      journal = {Iconic Research And Engineering Journals},
      year = {2024},
      volume = {7},
      number = {11},
      pages = {337-343},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1705807.pdf},
      abstract = {Diabetic Retinopathy (DR) is a serious diabetes complication leading to irreversible vision loss, thus emphasizing the need for early detection and intervention. Leveraging advancements in deep learning, this study investigates the application of a segmentation model based on the U-Net architecture for the early detection of DR. The U-Net model, renowned for its efficacy in semantic segmentation tasks, offers a promising approach to accurately delineating retinal structures indicative of DR-related lesions. Through comprehensive experimentation and evaluation, including the comparison of a standard U-Net segmentation model and U-Net with a VGG16 pre-trained encoder, termed U-NetVGG16, the models' proficiency in achieving high accuracy, precision, recall, Intersection over Union (IoU), and F1-score metrics was demonstrated. U-NetVGG16 excelled, earning a notable IoU of 98.62%, an accuracy of 99.10%, a precision of 99.30%, a recall of 99.40%, and an f1-score of 99.30%. The results highlight the potential of deep learning-based segmentation models in revolutionizing diabetic eye care by facilitating automated and precise identification of DR-related abnormalities. This study contributes to advancing the field of early DR detection, aiming to mitigate the global burden of preventable vision impairment associated with this debilitating condition.},
      month = {May},
  }