International Journal of Nonlinear Analysis and Applications

Analysis of deep learning methods in diabetic retinopathy disease identification based on retinal fundus image

Document Type : Research Paper

Authors

Department of Master in Informatic Engineering, Faculty of Computer Science and Information Technology, Universitas Sumatera Utara, Medan, Indonesia

Abstract

 Diabetic retinopathy (DR) is a serious retinal disease and is considered the leading cause of blindness and is strongly associated with people with diabetes. Ophthalmologists use optical coherence tomography (OCT) and retinal fundus imagery to assess the retinal thickness, structure, and also detecting edema, bleeding, and scarring. Deep learning models are used to analyze OCT or fundus images, extract unique features for each stage of DR, then identify images and determine the stage of the disease. Our research using retinal fundus imagery is used to identify diabetic retinopathy disease, among others, using the Convolutional Neural Network (CNN) method. The methodology stage in the study was a green channel, Contrast Limited Adaptive Histogram Equalization (CLAHE), morphological close, and background exclusion. Next, a segmentation process is carried out that aims to generate binary imagery using thresholding techniques. Then the binary image is used as training data conducted epoch as much as 30 times to obtain an optimal training model. After testing, the deep learning method with the CNN algorithm obtained 95.355\% accuracy in the identification of diabetic retinopathy disease based on fundus image in the retina.

Keywords

[1] M. Arora, M. Pandey, Deep neural network for diabetic retinopathy detection, In: 2019 Int. Conf. Mach. Learn. , Big Data, Cloud Parallel Comput. (Com-IT-Con), 2019, pp. 189-193.
[2] N. Chakrabarty, A deep learning method for the detection of diabetic retinopathy, In: 2018 5th IEEE Uttar Pradesh Sect. Int. Conf. Electr. Electron. Comput. Eng. (UPCON), 2018, pp. 1-5.
[3] A. Cruz-Roa, A. Basavanhally, F. Gonzales, H. Gilmore, M. Feldman, S. Ganesa, N. Shih, J. Tomaszewski and A. Madabhushi, Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks, In: Proc. SPIE 9041, Med. Imaging 2014: Digital Pathology, 904103.
[4] K. P. Danukusumo, Implementasi deep learning menggunakan convolutional neural network untuk klasifikasi citra candi berbasis Gpu, Fakultas Teknik Industri. Universitas Atma Jaya Yogyakarta. Yogyakarta 2017.
[5] M. Islam, A. V. Dinh and K. A. Wahid, Automated diabetic retinopathy detection using bag of words approach, J. Biomed. Sci. Eng., 10 (5)(2017), 86–96.
[6] C. Jayakumari, V. Lavanya, and E. P. Sumesh, Automated diabetic retinopathy detection and classification using imagenet convolution neural network using fundus images, Int. Conf. Smart Electron. Commun. (ICO SEC), 2020, pp. 577-582.
[7] P. Khojasteh, B. Aliahmad, S. P. Arjunan and D. K. Kumar, Introducing a novel layer in convolutional neural network for automatic identification of diabetic retinopathy, In: IEEE 2018 40th Annual Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBC) - Honolulu, HI, USA., 2018, pp. 5938–5941.
[8] T. Liu, S. Fang, Z. Yuehui, P. Wang and Jun. Zhang, Implementation of Training Convolutional Neural Networks, University of Chinese Academy of Sciences, 2015.
[9] P. Pal, S. Kundu, and A. K. Dhara, Detection of red lesions in retinal fundus images using YOLO V3, Curr. Indian Eye Res. J. Ophthalmic Res. Group, 7(2020) 49.
[10] M. Shaban, Z. Ogur, A. Mahmoud, A. Switala, A. Shalaby, H. Abu Khalifeh, M. Ghazal, L. Fraiwan, G. Giridharan, H. Sandhu, A. S. El-Baz, A convolutional neural network for the screening and staging of diabetic retinopathy. PLOS ONE, 15(6)(2020).
International Journal of Nonlinear Analysis and Applications
Volume 13, Issue 1
March 2022
Pages 1639-1647
  • Receive Date: 10 May 2021
  • Accept Date: 07 October 2021