Classification of Diabetic Retinopathy Disease Using Convolutional Neural Network

Agus Eko Minarno - Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
Mochammad Hazmi Cokro Mandiri - Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
Yufis Azhar - Faculty of Engineering, Universitas Muhammadiyah Malang, Indonesia
Fitri Bimantoro - Faculty of Engineering, Universitas Mataram, Indonesia
Hanung Adi Nugroho - Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta, Indonesia
Zaidah Ibrahim - Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia

Citation Format:



Diabetic Retinopathy (DR) is a disease that causes visual impairment and blindness in patients with it. Diabetic Retinopathy disease appears characterized by a condition of swelling and leakage in the blood vessels located at the back of the retina of the eye. Early detection through the retinal fundus image of the eye could take time and requires an experienced ophthalmologist. This study proposed a deep learning method, the Efficientnet-b7 model to identify diabetic retinopathy disease automatically. This study applies three preprocessing techniques that could be implemented in the dataset "APTOS 2019 Blindness Detection". In preprocessing technique trial scenarios, Usuyama preprocessing technique obtained the best results with accuracy of 89% of train data and 84% in test data compared to Harikrishnan preprocessing technique which has 82% accuracy in test data, and Ben Graham preprocessing has 81% accuracy in test data. In this study, Hyperparameter tuning was conducted to find the best parameters for use on the EfficientNet-B7 Model. In this study, we tested the Efficientnet-B7 model with an augmentation process that can reduce the occurrence of overfitting compared to models without augmentation. Preprocessing techniques and augmentation techniques can influence the proposed EfficientNet-B7 model in terms of performance results and reduce the overfitting of models.


Image; classification; diabetic retinopathy; CNN; APTOS.

Full Text:



S. Sridhar and S. Sanagavarapu, “Detection and Prognosis Evaluation of Diabetic Retinopathy using Ensemble Deep Convolutional Neural Networks,†IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., pp. 78–85, 2020, doi: 10.1109/IES50839.2020.9231789.

S. Sheikh and U. Qidwai, “Using MobileNetV2 to Classify the Severity of Diabetic Retinopathy,†Int. J. Simul. Syst. Sci. Technol., pp. 1–6, 2020, doi: 10.5013/ijssst.a.21.02.16.

H. Liu, K. Yue, S. Cheng, C. Pan, J. Sun, and W. Li, “Hybrid model structure for diabetic retinopathy classification,†J. Healthc. Eng., vol. 2020, 2020, doi: 10.1155/2020/8840174.

B. Tymchenko, “Deep Learning Approach to Diabetic Retinopathy Detection,†2019.

W. Zhang et al., “Automated identification and grading system of diabetic retinopathy using deep neural networks,†Knowledge-Based Syst., vol. 175, pp. 12–25, 2019, doi: 10.1016/j.knosys.2019.03.016.

R. Agarwal, A. Mahamuni, N. Gautam, P. Awachar, and P. Sagar, “Deep Learning-Based Grading of Diabetic Retinopathy Using Semantic Segmentation,†no. 4, pp. 4–6, 2020.

L. Qiao, Y. Zhu, and H. Zhou, “Diabetic Retinopathy Detection Using Prognosis of Microaneurysm and Early Diagnosis System for Non-Proliferative Diabetic Retinopathy Based on Deep Learning Algorithms,†IEEE Access, vol. 8, pp. 104292–104302, 2020, doi: 10.1109/ACCESS.2020.2993937.

Z. Gao, J. Li, J. Guo, Y. Chen, Z. Yi, and J. Zhong, “Diagnosis of Diabetic Retinopathy Using Deep Neural Networks,†IEEE Access, vol. 7, pp. 3360–3370, 2019, doi: 10.1109/ACCESS.2018.2888639.

A. Kwasigroch, B. Jarzembinski, and M. Grochowski, “Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy,†2018 Int. Interdiscip. PhD Work. IIPhDW 2018, pp. 111–116, 2018, doi: 10.1109/IIPHDW.2018.8388337.

E. Abdelmaksoud, S. Barakat, and M. Elmogy, “Diabetic retinopathy grading system based on transfer learning,†Int. J. Adv. Comput. Res., vol. 11, no. 52, pp. 1–12, 2021, doi: 10.19101/ijacr.2020.1048117.

V. K. Harikrishnan, M. Vijarania, and A. Gambhir, Diabetic retinopathy identification using autoML. Elsevier Inc., 2020.

K. Shankar, Y. Zhang, Y. Liu, L. Wu, and C. H. Chen, “Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification,†IEEE Access, vol. 8, pp. 118164–118173, 2020, doi: 10.1109/ACCESS.2020.3005152.

S. S. Chaturvedi, K. Gupta, V. Ninawe, and P. S. Prasad, “Automated Diabetic Retinopathy Identification Using Convolutional Neural Network,†Int. J. Adv. Trends Comput. Sci. Eng., vol. 10, no. 3, pp. 1872–1882, 2021, doi: 10.30534/ijatcse/2021/541032021.

S. Jayabalan, P. S. Pratheeksha, N. S. Bolar, and N. L. Malavika, “Prediction of Diabetic Retinopathy Using SVM Algorithm,†vol. 7, no. 14, pp. 1702–1711, 2020.

W. L. Alyoubi, M. F. Abulkhair, and W. M. Shalash, “Diabetic retinopathy fundus image classification and lesions localization system using deep learning,†Sensors, vol. 21, no. 11, pp. 1–22, 2021, doi: 10.3390/s21113704.

G. Mushtaq and F. Siddiqui, “Detection of diabetic retinopathy using deep learning methodology,†IOP Conf. Ser. Mater. Sci. Eng., vol. 1070, p. 012049, 2021, doi: 10.1088/1757-899x/1070/1/012049.

B. Graham, “Kaggle Diabetic Retinopathy Detection competition report,†pp. 1–9, 2015.

Ratthachat Chatpatanasiri, “APTOS : Eye Preprocessing in Diabetic Retinopathy.† (accessed Jul. 17, 2021).

J. L. Naoto Usuyama, “Medical image processing using Microsoft Deep Learning Framework (CNTK),†pp. 1–68, 2017.

C. Bhardwaj, S. Jain, and M. Sood, “Diabetic retinopathy severity grading employing quadrant-based Inception-V3 convolution neural network architecture,†Int. J. Imaging Syst. Technol., no. September, pp. 1–17, 2020, doi: 10.1002/ima.22510.

K. Xu, D. Feng, and H. Mi, “Deep convolutional neural network-based early automated detection of diabetic retinopathy using fundus image,†Molecules, vol. 22, no. 12, 2017, doi: 10.3390/molecules22122054.

W. Y. Lee, S. M. Park, and K. B. Sim, “Optimal hyperparameter tuning of convolutional neural networks based on the parameter-setting-free harmony search algorithm,†Optik (Stuttg)., vol. 172, no. July, pp. 359–367, 2018, doi: 10.1016/j.ijleo.2018.07.044.

Q. Xie, M. T. Luong, E. Hovy, and Q. V. Le, “Self-training with noisy student improves imagenet classification,†Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., pp. 10684–10695, 2020, doi: 10.1109/CVPR42600.2020.01070.