Early Detection of Diabetic Retinopathy with Segmentation Model U-Net

• Author(s): Adeyinka Mayowa-Majaro
• Paper ID: 1705807
• Page: 337-343
• Published Date: 16-05-2024
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 7 Issue 11 May-2024

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.

Citations

IRE Journals:
Adeyinka Mayowa-Majaro "Early Detection of Diabetic Retinopathy with Segmentation Model U-Net" Iconic Research And Engineering Journals Volume 7 Issue 11 2024 Page 337-343

IEEE:
Adeyinka Mayowa-Majaro "Early Detection of Diabetic Retinopathy with Segmentation Model U-Net" Iconic Research And Engineering Journals, vol. 7, no. 11, May. 2024

APA:
Adeyinka Mayowa-Majaro (2024). Early Detection of Diabetic Retinopathy with Segmentation Model U-Net. Iconic Research And Engineering Journals, 7(11).

MLA:
Adeyinka Mayowa-Majaro "Early Detection of Diabetic Retinopathy with Segmentation Model U-Net" Iconic Research And Engineering Journals, vol. 7, no. 11, May. 2024.

BibTeX

@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}
}