Deep Convolutional Neural Networks for Diabetic Retinopathy Screening

Document Type : Original Article


Electrical and Bioelectric Engineering Department, Khorasan Institute of Higher Education, Mashhad, Iran


Diabetic Retinopathy (DR) is one of the major complications of Diabetes, which is the injury to the retina of the diabetic patient and causes blindness if not diagnosed in early stages. Various machine learning classification and clustering approaches have been studied in literature with the purpose of improving the accuracy of the screening methods. Some of machine learning classification and clustering approaches are based on manually feature extraction of fundus images by image processing experts. In recent years, a new approach for image classification and diagnosis without using any manual feature extraction is proposed based on convolutional neural network (CNN). In medical imaging and diagnosis, training a deep CNN from scratch is difficult because it requires a large amount of labeled training data and the training procedure is a time consuming task to ensure proper convergence. Therefore, a very common method to train CNNs for medical diagnosis is fine-tuning a pre-trained CNN. In this paper, the pre-trained GoogleNet as a powerful CNN is employed on the Kaggle database for DR diagnosis from retinal images. To assess the efficacy of the clinical results, the proposed CNN algorithm is performed to diagnose DR from the images that are gathered from the the Navid-Didegan ophthalmology clinic.


[1]  X. Zhang and e. al., "Exudate detection in color retinal images for mass screening of diabetic retinopathy," Medical Image Analysis, vol. 18, pp. 1026-1043, October 2014.
[2]   K. S. Argade, K. A. Deshmukh, M. M. Narkhede, N. N. Sonawane, and S. Jore, "Automatic detection of diabetic retinopathy using image processing and data mining techniques," presented at the 2015 Int. Conference on Green Computing and Internet of Things, 2015.
[3] H. M. Zheng Y, Congdon N, "The worldwide epidemic of dneaiabetic retinopathy," Indian J Ophthalmol, vol. 60, pp. 428-431, 2012.
[4] B. Antal and A. Hajdu, "An ensemble-based system for Microaneurysm detection and diabetic retinopathy grading," IEEE Trans. Biomeical Engineering, vol. 59, pp. 1720–172, June 2012.
[5]   P. P. Conde, J. d. l. Calleja, A. Benitez, and M. A. Medina, "Image-based classification of diabetic retinopathy using machine learning," Int. Conf. Intell. Syst. Design and Applications, Nov., 2012.
[6]   M. U. Akram, S. Khalid, A. Tariq, and F. Azam, "Detection and classification of retinal lesions for grading of diabetic retinopathy," Computers in Biology and Med., vol. 45, pp. 161–171, Feb. 2014.
[7]   C. Sundhar and D. Archana, "Automatic screening of fundus images for detection of Diabetic Retinopathy," Int. J. Communication and Computer Technologies, vol. 2, April 2014.
[8]   A. F. M. Hani and H. A. Nugroho, "Gaussian Bayes classifier for medical diagnosis and grading: Application to diabetic retinopathy," IEEE Conf. Biomedical Engineering and Sciences, Nov. 2010.
[9]   B. v. G. M. Niemeijer, Michael J. Cree, "Retinopathy online challenge: Automatic detection of Microaneurysms in digital color Fundus photographs," IEEE Trans. Medical Imaging, vol. 29, pp. 185-195, Jan. 2010.
[10] S. S. Rahim, C. Jayne, V. Palade, and J. Shuttleworth, "Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening," Neural Computing and Applications, 2015.
[11] L. G. L. Giancardo, F. Meriaudeaub, T. P. Karnowskic, Y. L. G. Kenneth W. Tobin, J. C. EdwardChaumd, "Exudate-based diabetic macular edema detection in fundus images using publicly available datasets," Medical Image Analysis, vol. 16, pp. 216–226, Jan. 2012.
[12] C. Jayakumari and T. Santhanam, "Detection of hard exudates for diabetic retinopathy using contextual clustering and fuzzy art neural network," J. Information Technology, vol. 6, pp. 842-846, 2012.
[13] A. Osareh, B. Shadgar, and R. Markham, "A computational-intelligence-based approach for detection of exudates in diabetic retinopathy images," IEEE Trans. Information Technology in Biomedicine, vol. 13, pp. 535–545, July 2009.
[14] S. Franklin and S. Rajan, "Diagnosis of diabetic retinopathy by employing image processing technique to detect exudates in retinal images," IET Image Process., vol. 8, no. 10, pp. 601-609, 2014.
[15] G. B. Kande, T. S. Savithri, and P. V. Subbaiah, "Automatic detection of Microaneurysms and hemorrhages in digital Fundus images," J. Digital Imaging, vol. 23, pp. 430–437, November 2009.
[16] M. U. Akram, S. Khalid, A. Tariq, and M. Y. Javed, "Detection of neovascularization in retinal images using multivariate m-mediods based classifier," Computerized Medical Imaging and Graphics, vol. 37, pp. 346–357, July 2013.
[17] A. P. Bhatkar and G. U. Kharat, "Detection of diabetic Retinopathy in retinal images using MLP Classifier," IEEE Int. Symposium on Nanoelectronic and Information Syst., Dec. 2015.
[18] R. Priya and P. Aruna, "Diagnosis of diabetic retinopathy using machine learning techniques," J. Soft Computing, vol. 3, pp. 563–575, July 2013.
[19] K. Saranya, B. Ramasubramanian, and S. K. Mohideen, "A novel approach for the detection of new vessels in the retinal images for screening diabetic Retinopathy," Int. Conf. Communication and Signal Processing, April 2012.
[20] W. Zhang, R. Li, H. Deng, L. Wang, "Deep convolutional neural networks for multi-modality isointense infant brain image segmentation," NeuroImage, vol. 108, pp. 214-224, 2015.
[21] J. Y. Tajbakhsh, Suryakanth and R. Gurudu, "Convolutional neural networks for medical image analysis: fine tuning or full training?," IEEE Trans. Medical Imag., vol. 35, pp. 1299–1312, May 2016.
[22] D. Nie, L. Wang, E. Adeli, C. Lao, W. Lin and D. Shen, "3-D fully convolutional networks for multimodal isointense infant brain image segmentation," IEEE Trans. Cybern., vol. PP, no. 99, pp. 1-14, 2018.
[23] L. L. olger R. Roth, A. Farag, H. Shin, J. Liu, E. Turkbey and R. M. Summers, "Deeporgan: multi-level deep convolutional networks for automated pancreas segmentation," Medical Image Computing and Computer-Assisted. Lecture Notes in Computer Science, 2015.
[24] Y. Wanga, G. Caoa, B. Weib and G. Yang, "Hierarchical retinal blood vessel segmentation based on feature and ensemble learning," J. Neurocomput., vol. 149, pp. 708–717, Feb. 2015.
[25] F. C. Harry Pratta, Deborah M. Broadbentc, P. Simon P. Hardingac and Y. Zhengac, "Convolutional neural networks for diabetic retinopathy," International Conference On Medical Imaging Understanding and Analysis, July 2016.
[26] H. H. Vo and A. Verma, "New deep neural nets for fine-grained diabetic retinopathy recognition on hybrid color space," IEEE Int. Symposium on Multimedia, Dec. 2016.
[27] Y. M. S. Reddy, R. E. Ravindran and K. H. Kishore, "Diabetic retinopathy through retinal image analysis: A review," Int. J. of Engineering Technology, vol. 7, no. 1-5, p. 19, 2017.
[28] B. a. Antal and A. a. Hajdu, "An ensemble-based system for automatic screening of diabetic retinopathy," Knowledge-Based Systems, Elsevier, vol. 60, pp. 20-27, April 2014.
[29] L. Ryan, T. Y. Wong, and C. Sabanayagam. “Epidemiology of diabetic retinopathy, diabetic macular edema and related vision loss,” Eye and Vision 2 (2015): 17. PMC. Web. 5 Sept. 2017.
[30] D. H. Hubel and T. N. Wiesel, "Receptive fields and functional architecture of monkey striate cortex," J. Physiol, vol. 195, pp. 215–24, Jun. 1968.
[31] K. Fukushima, "Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position," Biological Cybernetics, vol. 36, no. 4, pp. 193–202, 1980.
[32] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with deep convolutional neural networks," Conf. Neural Inf. Processing Syst. (NIPS), 2012.
[33] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient based learning applied to document recognition," Proc. IEEE, vol. 86, pp. 2278–2324, 1998.
[34] [Online]. Available: 1207.0580. Accessed: Nov. 10, 2016.
[35] C. Szegedy et al., "Going deeper with convolutions," IEEE Conf. on computer Vision and Pattern Recognition (CVPR), 2015.
[36] C. Szegedy et al., "Rethinking the Inception Architecture for Computer Vision," IEEE Conf. on Computer Vision, Dec. 2015.
[37] T. Fawcett, "An introduction to ROC analysis," Pattern Recognition Letters, vol. 27, no. 6, pp. 861-874, Jun. 2006.
[38] [Online]. Available: https://kaggle2.blob. core. attachments/88655 /2795/ competitionreport.pdf. Accessed: Jul. 5, 2017.