Classifying Diabetic Retinopathy using CNN and Machine Learning

Chaymaa Lahmar, Ali Idri, Ali Idri

2022

Abstract

Diabetic retinopathy (DR) is one of the main causes of vision loss around the world. A computer-aided diagnosis can help in the early detection of this disease which can be beneficial for a better patient outcome. In this paper, we conduct an empirical evaluation of the performances of twenty-eight deep hybrid architectures for an automatic binary classification of referable DR, and compared them to seven end-to-end deep learning (DL) architectures. The architectures were compared using the Scott Knott test and the Borda count voting method. All the empirical evaluations were over the APTOS dataset, using five-fold cross validation. The results showed the importance of combining DL techniques and classical machine learning techniques for the classification of DR. The hybrid architecture using the SVM classifier and MobileNet_V2 for feature extraction was the top performing and it was classified among the best performing end-to-end deep learning architectures with an accuracy equal to 88.80%; note that none of the hybrid architectures outperformed all the end-to-end architectures.

Paper Citation

in Harvard Style

Lahmar C. and Idri A. (2022). Classifying Diabetic Retinopathy using CNN and Machine Learning. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING; ISBN 978-989-758-552-4, SciTePress, pages 52-62. DOI: 10.5220/0010851500003123

in Bibtex Style

@conference{bioimaging22,
author={Chaymaa Lahmar and Ali Idri},
title={Classifying Diabetic Retinopathy using CNN and Machine Learning},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING},
year={2022},
pages={52-62},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010851500003123},
isbn={978-989-758-552-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - Volume 2: BIOIMAGING
TI - Classifying Diabetic Retinopathy using CNN and Machine Learning
SN - 978-989-758-552-4
AU - Lahmar C.
AU - Idri A.
PY - 2022
SP - 52
EP - 62
DO - 10.5220/0010851500003123
PB - SciTePress