Using Deep Learning on Retinal Images to Classify the Severity of Diabetic Retinopathy

Diabetic retinopathy (DR) is a leading cause of blindness worldwide, particularly among working-age individuals. With the increasing prevalence of diabetes, there is an urgent need to address the public health burden posed by DR. This research paper aims to develop a clinical decision support approach that integrates automated DR detection and classifying the grade of severity in DR. A three-stage deep learning model for DR detection is proposed. First, incorporating preprocessing, image enhancement, and augmenting the DR images using three different color space transformations and a filtering technique: BGR to RGB, RGR to LAB, and Gaussian Blur Filter. Secondly, feature extraction and representation learning are based on CNN with various layers. Thirdly, classification is based on SVM. The implementation and evaluation of the proposed model on a dataset containing five stages of DR are essential steps towards validating its performance and assessing its potential for clinical applications. Through thorough dataset preprocessing, model training, performance analysis, comparison with baseline methods, and generalization tests, we can gain insights into the model's classification and staging capabilities. This research makes a significant contribution to the field of DR severity detection, ultimately leading to enhanced diagnostic capabilities. The developed models demonstrated an accuracy rate of 94.72%, indicating their efficacy in accurately assessing the severity of the condition.

@article{El-aal2024,
  title     = {Using Deep Learning on Retinal Images to Classify the Severity of Diabetic Retinopathy},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {7},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Shereen A. El-aal and Rania Salah El-Sayed and Abdulellah Abdullah Alsulaiman and Mohammed Abdel Razek},
  doi       = {10.14569/IJACSA.2024.0150734},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150734}
}