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DOI: 10.14569/IJACSA.2022.0130798
PDF

Prediction of Diabetic Retinopathy using Convolutional Neural Networks

Author 1: Manal Alsuwat
Author 2: Hana Alalawi
Author 3: Shema Alhazmi
Author 4: Sarah Al-Shareef

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 7, 2022.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Diabetic retinopathy (DR) is among the most dan-gerous diabetic complications that can lead to lifelong blindness if left untreated. One of the essential difficulties in DR is early discovery, which is crucial for therapy progress. The accurate diagnosis of the DR stage is famously complicated and demands a skilled analysis by the expert being of fundus images. This paper detects DR and classifies its stage using retina images by applying conventional neural networks and transfer learning models. Three deep learning models were investigated: trained from scratch CNN and pre-trained InceptionV3 and Efficient-NetsB5. Experiment results show that the proposed CNN model outperformed the pre-trained models with a 9 to 25% relative improvement in F1-score compared to pre-trained InceptionV3 and EfficientNetsB5, respectively.

Keywords: CNN; convolutional neural networks; deep learn-ing; transfer learning; medical imaging; diabetic retinopathy; retina fundus images

Manal Alsuwat, Hana Alalawi, Shema Alhazmi and Sarah Al-Shareef, “Prediction of Diabetic Retinopathy using Convolutional Neural Networks” International Journal of Advanced Computer Science and Applications(IJACSA), 13(7), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130798

@article{Alsuwat2022,
title = {Prediction of Diabetic Retinopathy using Convolutional Neural Networks},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130798},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130798},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {7},
author = {Manal Alsuwat and Hana Alalawi and Shema Alhazmi and Sarah Al-Shareef}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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