A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification

Authors

Omer Faruk Gurcan^{1, *,} , Omer Faruk Beyca^1, , Onur Dogan^{2, 3,}

¹Department of Industrial Engineering, Istanbul Technical University, Istanbul, 34367, Turkey

²Department of Industrial Engineering, Izmir Bakircay University, Izmir, 35665, Turkey

³Research Center for Data Analytics and Spatial Data Modeling (RC-DAS), Izmir Bakircay University, Izmir, 35665, Turkey

^*Corresponding author. Email: ofgurcan@itu.edu.tr

DOI

10.2991/ijcis.d.210316.001How to use a DOI?

Keywords

machine learning; ensemble learning; transfer learning; XGBoost; PCA; SVD

Abstract

Diabetes is one of the emerging threats to public health all over the world. According to projections by the World Health Organization, diabetes will be the seventh foremost cause of death in 2030 (WHO, Diabetes, 2020. https://www.afro.who.int/health-topics/diabetes). Diabetic retinopathy (DR) results from long-lasting diabetes and is the fifth leading cause of visual impairment, worldwide. Early diagnosis and treatment processes are critical to overcoming this disease. The diagnostic procedure is challenging, especially in low-resource settings, or time-consuming, depending on the ophthalmologist's experience. Recently, automated systems now address DR classification tasks. This study proposes an automated DR classification system based on preprocessing, feature extraction, and classification steps using deep convolutional neural network (CNN) and machine learning methods. Features are extracted from a pretrained model by the transfer learning approach. DR images are classified by several machine learning methods. XGBoost outperforms other methods. Dimensionality reduction algorithms are applied to obtain a lower-dimensional representation of extracted features. The proposed model is trained and evaluated on a publicly available dataset. Grid search and calibration are used in the analysis. This study provides researchers with performance comparisons of different machine learning methods. The proposed model offers a robust solution for detecting DR with a small number of images. We used a transfer learning approach, which differs from other studies in the literature, during the feature extraction. It provides a data-driven, cost-effective solution, which includes comprehensive preprocessing and fine-tuning processes.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Omer Faruk Gurcan
AU  - Omer Faruk Beyca
AU  - Onur Dogan
PY  - 2021
DA  - 2021/03/22
TI  - A Comprehensive Study of Machine Learning Methods on Diabetic Retinopathy Classification
JO  - International Journal of Computational Intelligence Systems
SP  - 1132
EP  - 1141
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.210316.001
DO  - 10.2991/ijcis.d.210316.001
ID  - Gurcan2021
ER  -