Main Article Content

Emmy Bhatti
Prabhpreet Kaur


Diabetic Retinopathy is the most common form of diabetic eye disease. Diabetic Retinopathy (DR) usually only affects people who have had diabetes (diagnosed or undiagnosed) for a significant number of years. In spite of DR not being a curable illness, it can be treated if it is detected in its early stages. The main objective of this paper is to review the various deep learning, machine learning approaches in correspondence with image processing for diagnosing Diabetic Retinopathy as early as possible. Firstly, we have a look at how automated detection systems are constructed and highlight various features used for the purpose of DR diagnosis. Next we analyze the existing work in the field focusing both techniques individually. The papers have been compared and contrasted based on qualitative and qualitative parameters viz., purpose of the work, algorithms adopted and results obtained. Finally, we present an overall comparison between deep learning and machine learning algorithms used to detect diabetic retinopathy and conclude why deep learning approaches provide best results when it comes to early detection of the disease.


Download data is not yet available.

Article Details



Tan, J. H., Fujita, H., Sivaprasad, S., Bhandary, S. V., Rao, A. K., Chua, K. C., & Acharya, U. R. (2017). Automated segmentation of exudates, haemorrhages, microaneurysms using single convolutional neural network. Information Sciences, 420, 66-76. DOI: 10.1016/j.ins.2017.08.050

van Grinsven, M. J., van Ginneken, B., Hoyng, C. B., Theelen, T., & Sánchez, C. I. (2016). Fast convolutional neural network training using selective data sampling: Application to hemorrhage detection in color fundus images. IEEE transactions on medical imaging, 35(5), 1273-1284. DOI 10.1109/TMI.2016.2526689

Abbas, Q., Fondon, I., Sarmiento, A., Jiménez, S., & Alemany, P. (2017). Automatic recognition of severity level for diagnosis of diabetic retinopathy using deep visual features. Medical & Biological Engineering & Computing, 1-16. DOI 10.1007/s11517-017-1638-6

Doshi, D., Shenoy, A., Sidhpura, D., & Gharpure, P. (2016, December). Diabetic retinopathy detection using deep convolutional neural networks. In Computing, Analytics and Security Trends (CAST), International Conference on (pp. 261-266). IEEE. DOI: 10.1109/CAST.2016.7914977

Quellec, G., Charrière, K., Boudi, Y., Cochener, B., & Lamard, M. (2017). Deep image mining for diabetic retinopathy screening. Medical Image Analysis, 39, 178-193.DOI: 10.1016/j.media.2017.04.012

Pratt, H., Coenen, F., Broadbent, D. M., Harding, S. P., & Zheng, Y. (2016). Convolutional neural networks for diabetic retinopathy. Procedia Computer Science, 90, 200-205. DOI: 10.1016/j.procs.2016.07.014

Gargeya, R., & Leng, T. (2017). Automated Identification of Diabetic Retinopathy Using Deep Learning. Ophthalmology. DOI: 10.1016/j.ophtha.2017.02.008 ISSN

Colas, E., Besse, A., Orgogozo, A., Schmauch, B., Meric, N., & Besse, E. (2016). Deep learning approach for diabetic retinopathy screening. Acta Ophthalmologica, 94(S256). DOI: 10.1111/j.1755-3768.2016.0635

Roy, P., Tennakoon, R., Cao, K., Sedai, S., Mahapatra, D., Maetschke, S., & Garnavi, R. (2017, April). A novel hybrid approach for severity assessment of Diabetic Retinopathy in colour fundus images. In, 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (pp. 1078-1082). IEEE. DOI: 10.1109/ISBI.2017.7950703

Paul, S., & Singh, L. (2016, December). Heterogeneous modular deep neural network for diabetic retinopathy detection. In Humanitarian Technology Conference (R10-HTC), 2016 IEEE Region 10 (pp. 1-6). IEEE. DOI: 10.1109/R10-HTC.2016.7906821

Takahashi, H., Tampo, H., Arai, Y., Inoue, Y., & Kawashima, H. (2017). Applying artificial intelligence to disease staging: Deep learning for improved staging of diabetic retinopathy. PloS one, 12(6), e0179790. DOI: 10.1371/journal.pone.0179790

ElTanboly, A., Ismail, M., Shalaby, A., Switala, A., Elâ€Baz, A., Schaal, S., ... & Elâ€Azab, M. (2017). A computerâ€aided diagnostic system for detecting diabetic retinopathy in optical coherence tomography images. Medical physics, 44(3), 914-923. DOI: 10.1002/mp.12071

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., ... & Kim, R. (2016). Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama, 316(22), 2402-2410. DOI: 10.1001/jama.2016.17216

PrentaÅ¡ić, P., & LonÄarić, S. (2016). Detection of exudates in fundus photographs using deep neural networks and anatomical landmark detection fusion. Computer methods and programs in biomedicine, 137, 281-292. DOI: 10.1016/j.cmpb.2016.09.018

PrentaÅ¡ić, P., & LonÄarić, S. (2015, September). Detection of exudates in fundus photographs using convolutional neural networks. In Image and Signal Processing and Analysis (ISPA), 2015 9th International Symposium on (pp. 188-192). IEEE. DOI: 10.1109/ISPA.2015.7306056

Shan, J., & Li, L. (2016, June). A deep learning method for microaneurysm detection in fundus images. In Connected Health: Applications, Systems and Engineering Technologies (CHASE), 2016 IEEE First International Conference on (pp. 357-358). IEEE. DOI 10.1109/CHASE.2016.12

Costa, P., & Campilho, A. (2017). Convolutional bag of words for diabetic retinopathy detection from eye fundus images. IPSJ Transactions on Computer Vision and Applications, 9(1), 10. DOI: 10.1186/s41074-017-0023-6

Vo, H. H., & Verma, A. (2016, December). New Deep Neural Nets for Fine-Grained Diabetic Retinopathy Recognition on Hybrid Color Space. In Multimedia (ISM), 2016 IEEE International Symposium on (pp. 209-215). IEEE. DOI 10.1109/ISM.2016.99

Rahim, S. S., Palade, V., Shuttleworth, J., & Jayne, C. (2016). Automatic screening and classification of diabetic retinopathy and maculopathy using fuzzy image processing. Brain informatics, 3(4), 249-267. DOI 10.1007/s00521-015-1929-5

Antal, B., & Hajdu, A. (2012). An ensemble-based system for microaneurysm detection and diabetic retinopathy grading. IEEE transactions on biomedical engineering, 59(6), 1720-1726. DOI: 10.1109/TBME.2012.2193126

Purandare, M., & Noronha, K. (2016, November). Hybrid system for automatic classification of Diabetic Retinopathy using fundus images. In Green Engineering and Technologies (IC-GET), 2016 Online International Conference on (pp. 1-5). IEEE. DOI: 10.1109/GET.2016.7916623

Labhade, J. D., Chouthmol, L. K., & Deshmukh, S. (2016, September). Diabetic retinopathy detection using soft computing techniques. In Automatic Control and Dynamic Optimization Techniques (ICACDOT), International Conference on (pp. 175-178). IEEE. DOI: 10.1109/ICACDOT.2016.7877573

Sathananthavathi, V., Indumathi, G., & Rajalakshmi, R. (2017, March). Abnormalities detection in retinal fundus images. In Inventive Communication and Computational Technologies (ICICCT), 2017 International Conference on (pp. 89-93). IEEE. DOI: 10.1109/ICICCT.2017.7975165

Ganesan, K., Martis, R. J., Acharya, U. R., Chua, C. K., Min, L. C., Ng, E. Y. K., & Laude, A. (2014). Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. Medical & biological engineering & computing, 52(8), 663-672. DOI 10.1007/s11517-014-1167-5

Kumar, P. S., Deepak, R. U., Sathar, A., Sahasranamam, V., & Kumar, R. R. (2016). Automated Detection System for Diabetic Retinopathy Using Two Field Fundus Photography. Procedia Computer Science, 93, 486-494. DOI : 10.1016/j.procs.2016.07.237

Lachure, J., Deorankar, A. V., Lachure, S., Gupta, S., & Jadhav, R. (2015, June). Diabetic Retinopathy using morphological operations and machine learning. In Advance Computing Conference (IACC), 2015 IEEE International (pp. 617-622). IEEE. DOI: 10.1109/IADCC.2015.7154781

Mamilla, R. T., Ede, V. K. R., & Bhima, P. R. (2017). Extraction of Microaneurysms and Hemorrhages from Digital Retinal Images. Journal of Medical and Biological Engineering, 37(3), 395-408. DOI 10.1007/s40846-017-0237-1

Pires, R., Jelinek, H. F., Wainer, J., Goldenstein, S., Valle, E., & Rocha, A. (2013). Assessing the need for referral in automatic diabetic retinopathy detection. IEEE Transactions on Biomedical Engineering, 60(12), 3391-3398. DOI: 10.1109/TBME.2013.2278845

Pires, R., Avila, S., Jelinek, H. F., Wainer, J., Valle, E., & Rocha, A. (2017). Beyond lesion-based diabetic retinopathy: a direct approach for referral. IEEE journal of biomedical and health informatics, 21(1), 193-200. DOI 10.1109/JBHI.2015.2498104

Rahim, S. S., Jayne, C., Palade, V., & Shuttleworth, J. (2016). Automatic detection of microaneurysms in colour fundus images for diabetic retinopathy screening. Neural Computing and Applications, 27(5), 1149-1164, DOI 10.1007/s40708-016-0045-3.

Roychowdhury, S., Koozekanani, D. D., & Parhi, K. K. (2014). DREAM: diabetic retinopathy analysis using machine learning. IEEE journal of biomedical and health informatics, 18(5), 1717-1728. DOI 10.1109/JBHI.2013.2294635

Zhou, W., Wu, C., Chen, D., Yi, Y., & Du, W. (2017). Automatic Microaneurysm Detection Using the Sparse Principal Component Analysis-Based Unsupervised Classification Method. IEEE Access, 5, 2563-2572. DOI 10.1109/ACCESS.2017.267191

Harini, R., & Sheela, N. (2016, August). Feature extraction and classification of retinal images for automated detection of Diabetic Retinopathy. In Cognitive Computing and Information Processing (CCIP), 2016 Second International Conference on (pp. 1-4). IEEE. DOI: 10.1109/CCIP.2016.7802862

Mahendran, G., & Dhanasekaran, R. (2015). Investigation of the severity level of diabetic retinopathy using supervised classifier algorithms. Computers & Electrical Engineering, 45, 312-323. DOI: 10.1016/j.compeleceng.2015.01.013

Gegundez–Arias, M. E., Marin, D., Ponte, B., Alvarez, F., Garrido, J., Ortega, C., & Bravo, J. M. (2017). A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis. Computers in Biology and Medicine. DOI:10.1016/j.compbiomed.2017.07.007

Somfai, G. M., Tátrai, E., Laurik, L., Varga, B., Ölvedy, V., Jiang, H., & DeBuc, D. C. (2014). Automated classifiers for early detection and diagnosis of retinopathy in diabetic eyes. BMC bioinformatics, 15(1), 106. DOI: 10.1186/1471-2105-15-106