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.

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}
}