Skip to main content
SpringerLink
Log in

EyeDeep-Net: a multi-class diagnosis of retinal diseases using deep neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Retinal images are a key element for ophthalmologists in diagnosing a variety of eye illnesses. The retina is vulnerable to microvascular changes as a result of many retinal diseases and a variety of research have been done on early diagnosis of medical images to take proper treatment on time. This paper designs an automated deep learning-based non-invasive framework to diagnose multiple eye diseases using colour fundus images. A multi-class eye disease RFMiD dataset was used to develop an efficient diagnostic framework. Multi-class fundus images were extracted from a multi-label dataset and then various augmentation techniques were applied to make the framework robust in real-time. Images were processed according to the network for low computational demand. A multi-layer neural network EyeDeep-Net has been developed to train and test images for diagnosis of various eye problems in which the keystone convolutional neural network extracts relevant features from the input colour fundus image dataset and then processed features were used to make predictive diagnostic decisions. The strength of the EyeDeep-Net is evaluated using multiple statistical parameters and the performance of the proposed model is found to be significantly superior to multiple baseline state-of-the-art models. A comprehensive comparison of the proposed methodology to the most recent methods proves its efficacy in terms of classification and disease identification through digital fundus images.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

Data availability

The datasets analysed during the current study are publicly available in the Retinal Fundus Multi-Disease Image Dataset (RFMiD repository available at https://riadd.grand-challenge.org/download-all-classes or https://doi.org/10.3390/data6020014.

References

  1. https://www.who.int/publications/i/item/world-report-on-vision, Accessed 25 Feb 2022

  2. Jonas JB, Bourne RRA, White RA, Flaxman SR, Keeffe J, Leasher J, Naidoo K, Pesudovs K, Price H, Wong TY, Resnikoff S, Taylor HR (2014) visual Impairment and blindness due to macular diseases globally: a systematic review and meta-analysis. Am J Ophthalmol 158:808–815. https://doi.org/10.1016/j.ajo.2014.06.012

    Article  Google Scholar 

  3. Michaud L, Forcier P (2014) Prevalence of asymptomatic ocular conditions in subjects with refractive-based symptoms. J Optom 7:153–160. https://doi.org/10.1016/j.optom.2013.08.003

    Article  Google Scholar 

  4. Méjécase C, Malka S, Guan Z, Slater A, Arno G, Moosajee M (2020) Practical guide to genetic screening for inherited eye diseases. Ther Adv Ophthalmol 12:251584142095459. https://doi.org/10.1177/2515841420954592

    Article  Google Scholar 

  5. Leasher JL, Bourne RRA, Flaxman SR, Jonas JB, Keeffe J, Naidoo K, Pesudovs K, Price H, White RA, Wong TY, Resnikoff S, Taylor HR (2016) Global estimates on the number of people blind or visually impaired by diabetic retinopathy: a meta-analysis from 1990 to 2010. Diabetes Care 39:1643–1649. https://doi.org/10.2337/dc15-2171

    Article  Google Scholar 

  6. Walton OB, Garoon RB, Weng CY, Gross J, Young AK, Camero KA, Jin H, Carvounis PE, Coffee RE, Chu YI (2016) Evaluation of automated teleretinal screening program for diabetic retinopathy. JAMA Ophthalmol 134:204. https://doi.org/10.1001/jamaophthalmol.2015.5083

    Article  Google Scholar 

  7. Riazi Esfahani H, Hajizadeh F (2021) Age-related macular degeneration (ARMD). Diagnostics in Ocular Imaging. Springer, Cham, pp 463–476. https://doi.org/10.1007/978-3-030-54863-6_17

    Chapter  Google Scholar 

  8. Malick H, Din N, Rajput R, Mushtaq B (2021) Cataract surgery in patients with neovascular age related macular degeneration—examination of current practice among UK ophthalmic surgeons. Eye 35:685–686. https://doi.org/10.1038/s41433-020-0863-7

    Article  Google Scholar 

  9. Hayreh SS (1974) Pathogenesis of cupping of the optic disc. Br J Ophthalmol 58:863–876. https://doi.org/10.1136/bjo.58.10.863

    Article  Google Scholar 

  10. Bressler NM (2004) Age-related macular degeneration is the leading cause of blindness. JAMA 291:1900. https://doi.org/10.1001/jama.291.15.1900

    Article  Google Scholar 

  11. Ye H, Zhang Q, Liu X, Cai X, Yu W, Yu S, Wang T, Lu W, Li X, Jin H, Hu Y, Kang X, Zhao P (2014) Prevalence of age-related macular degeneration in an elderly urban Chinese population in china: the Jiangning eye study. Investig Opthalmology Vis Sci 55:6374. https://doi.org/10.1167/iovs.14-14899

    Article  Google Scholar 

  12. Edupuganti VG, Chawla A, Kale A (2018) Automatic optic disk and cup segmentation of fundus images using deep learning. In 2018 25th IEEE international conference on image processing (ICIP), pp 2227–2231

  13. Dias JM, Oliveira CM, da Silva Cruz LA (2014) Retinal image quality assessment using generic image quality indicators. Inf Fusion 19:73–90. https://doi.org/10.1016/j.inffus.2012.08.001

    Article  Google Scholar 

  14. Cui H, Shen S, Gao W, Liu H, Wang Z (2019) Efficient and robust large-scale structure-from-motion via track selection and camera prioritization. ISPRS J Photogramm Remote Sens 156:202–214. https://doi.org/10.1016/j.isprsjprs.2019.08.005

    Article  Google Scholar 

  15. Peli E, Peli T (1989) Restoration of retinal images obtained through cataracts. IEEE Trans Med Imaging 8:401–406. https://doi.org/10.1109/42.41493

    Article  Google Scholar 

  16. Singh A, Dutta MK, ParthaSarathi M, Uher V, Burget R (2016) Image processing based automatic diagnosis of glaucoma using wavelet features of segmented optic disc from fundus image. Comput Methods Programs Biomed 124:108–120. https://doi.org/10.1016/j.cmpb.2015.10.010

    Article  Google Scholar 

  17. Singh A, Dutta MK, Parthasarathi M, Burget R, Riha K (2014) An efficient automatic method of Optic disc segmentation using region growing technique in retinal images. In: 2014 International conference on contemporary computing and informatics (IC3I), pp 480–484. IEEE

  18. Issac SMA, Dutta MK (2018) An automated and robust image processing algorithm for glaucoma diagnosis from fundus images using novel blood vessel tracking and bend point detection. Int J Med Inform 110:52–70. https://doi.org/10.1016/j.ijmedinf.2017.11.015

    Article  Google Scholar 

  19. Soni A, Rai A (2021) Automatic cataract detection using sobel and morphological dilation operation. In: Proceedings of research and applications in artificial intelligence, pp 267–276. https://doi.org/10.1007/978-981-16-1543-6_25

  20. Issac A, Partha Sarathi M, Dutta MK (2015) An adaptive threshold based image processing technique for improved glaucoma detection and classification. Comput Methods Programs Biomed 122:229–244. https://doi.org/10.1016/j.cmpb.2015.08.002

    Article  Google Scholar 

  21. Ganguly S, Ganguly S, Srivastava K, Dutta MK, Parthasarathi M, Burget R, Riha K (2014) An adaptive threshold based algorithm for detection of red lesions of diabetic retinopathy in a fundus image. In: 2014 International conference on medical imaging, m-health and emerging communication systems (MedCom) pp 91–94. https://doi.org/10.1109/MedCom.2014.7005982

  22. Wang W (2022) Further results on mean-square exponential input-to-state stability of stochastic delayed cohen-grossberg neural networks. Neural Process Lett. https://doi.org/10.1007/s11063-022-10974-8

    Article  Google Scholar 

  23. Huang C, Liu B, Yang H, Cao J (2022) Positive almost periodicity on SICNNs incorporating mixed delays and D operator. Nonlinear Anal Model Control 27:1–21

    Article  MATH  MathSciNet  Google Scholar 

  24. Long S, Chen J, Hu A, Liu H, Chen Z, Zheng D (2020) Microaneurysms detection in color fundus images using machine learning based on directional local contrast. Biomed Eng Online 19:21. https://doi.org/10.1186/s12938-020-00766-3

    Article  Google Scholar 

  25. Rekhi RS, Issac A, Dutta MK, Travieso CM (2017) Automated classification of exudates from digital fundus images. In: 2017 International conference and workshop on bioinspired intelligence (IWOBI) pp 1–6. https://doi.org/10.1109/IWOBI.2017.7985527

  26. García-Floriano A, Ferreira-Santiago Á, Camacho-Nieto O, Yáñez-Márquez C (2019) A machine learning approach to medical image classification: detecting age-related macular degeneration in fundus images. Comput Electr Eng 75:218–229. https://doi.org/10.1016/j.compeleceng.2017.11.008

    Article  Google Scholar 

  27. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005

    Article  Google Scholar 

  28. Zhao W, Yang J, Sun Y, Li C, Wu W, Jin L, Yang Z, Ni B, Gao P, Wang P, Hua Y, Li M (2018) 3D Deep learning from CT scans predicts tumor invasiveness of subcentimeter pulmonary adenocarcinomas. Cancer Res 78:6881–6889. https://doi.org/10.1158/0008-5472.CAN-18-0696

    Article  Google Scholar 

  29. Yang J, Deng H, Huang X, Ni B, Xu Y (2020) Relational learning between multiple pulmonary nodules via deep set attention transformers. In: 2020 IEEE 17th international symposium on biomedical imaging (ISBI), pp 1875–1878. https://doi.org/10.1109/ISBI45749.2020.9098722

  30. Roy AG, Conjeti S, Karri SPK, Sheet D, Katouzian A, Wachinger C, Navab N (2017) ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks. Biomed Opt Express 8:3627. https://doi.org/10.1364/BOE.8.003627

    Article  Google Scholar 

  31. Hu K, Zhang Z, Niu X, Zhang Y, Cao C, Xiao F, Gao X (2018) Retinal vessel segmentation of color fundus images using multiscale convolutional neural network with an improved cross-entropy loss function. Neurocomputing 309:179–191. https://doi.org/10.1016/j.neucom.2018.05.011

    Article  Google Scholar 

  32. Playout C, Duval R, Cheriet F (2019) A novel weakly supervised multitask architecture for retinal lesions segmentation on fundus images. IEEE Trans Med Imaging 38:2434–2444. https://doi.org/10.1109/TMI.2019.2906319

    Article  Google Scholar 

  33. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR (2016) Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316:2402. https://doi.org/10.1001/jama.2016.17216

    Article  Google Scholar 

  34. Li Z, He Y, Keel S, Meng W, Chang RT, He M (2018) Efficacy of a deep learning system for detecting glaucomatous optic neuropathy based on color fundus photographs. Ophthalmology 125:1199–1206. https://doi.org/10.1016/j.ophtha.2018.01.023

    Article  Google Scholar 

  35. . Govindaiah A, Hussain MA, Smith RT, Bhuiyan A (2018) Deep convolutional neural network based screening and assessment of age-related macular degeneration from fundus images. In: 2018 IEEE 15th International symposium on biomedical imaging (ISBI 2018) IEEE pp 1525–1528. https://doi.org/10.1109/ISBI.2018.8363863

  36. Song W, Cao Y, Qiao Z, Wang Q, Yang JJ (2019) An improved semi-supervised learning method on cataract fundus image classification. In: 2019 IEEE 43rd annual computer software and applications conference (COMPSAC): pp 362–367. https://doi.org/10.1109/COMPSAC.2019.10233

  37. Karri SPK, Chakraborty D, Chatterjee J (2017) Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed Opt Express 8:579. https://doi.org/10.1364/BOE.8.000579

    Article  Google Scholar 

  38. Li X, Pang T, Xiong B, Liu W, Liang P, Wang T (2017) Convolutional neural networks based transfer learning for diabetic retinopathy fundus image classification. In: 2017 10th international congress on image and signal processing, biomedical engineering and informatics (CISP-BMEI), IEEE, pp 1–11. https://doi.org/10.1109/CISP-BMEI.2017.8301998

  39. Joshi RC, Dutta MK, Sikora P, Kiac M (2020) Efficient convolutional neural network based optic disc analysis using digital fundus images. In: 2020 43rd international conference on telecommunications and signal processing (TSP), IEEE pp 533–536. https://doi.org/10.1109/TSP49548.2020.9163560.

  40. Sarki R, Ahmed K, Wang H, Zhang Y (2020) Automated detection of mild and multi-class diabetic eye diseases using deep learning. Heal Inf Sci Syst 8:32. https://doi.org/10.1007/s13755-020-00125-5

    Article  Google Scholar 

  41. Sahlsten J, Jaskari J, Kivinen J, Turunen L, Jaanio E, Hietala K, Kaski K (2019) Deep learning fundus image analysis for diabetic retinopathy and macular edema grading. Sci Rep 9:10750. https://doi.org/10.1038/s41598-019-47181-w

    Article  Google Scholar 

  42. T. Wu, L. Liu, T. Zhang, X. Wu, Deep learning-based risk classification and auxiliary diagnosis of macular edema, Intell. Med. 6 (2022) 100053. https://doi.org/10.1016/j.ibmed.2022.100053.

  43. Kumar S, Kumar B (2022) Automatic early glaucoma detection by extracting parapapillary atrophy and optic disc from fundus image using SVM. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-11023-7

    Article  Google Scholar 

  44. Purna Chandra Reddy V, Gurrala KK (2022) Joint DR-DME classification using deep learning-CNN based modified grey-wolf optimizer with variable weights. Biomed Signal Process Control 73:103439

    Article  Google Scholar 

  45. Veena HN, Muruganandham A, Senthil Kumaran T (2022) A novel optic disc and optic cup segmentation technique to diagnose glaucoma using deep learning convolutional neural network over retinal fundus images. J King Saud Univ Comput Inf Sci 34:6187–6198. https://doi.org/10.1016/j.jksuci.2021.02.003

    Article  Google Scholar 

  46. Li F, Wang Y, Xu T, Dong L, Yan L, Jiang M, Zhang X, Jiang H, Wu Z, Zou H (2022) Deep learning-based automated detection for diabetic retinopathy and diabetic macular oedema in retinal fundus photographs. Eye 36:1433–1441. https://doi.org/10.1038/s41433-021-01552-8

    Article  Google Scholar 

  47. Pascal L, Perdomo OJ, Bost X, Huet B, Otálora S, Zuluaga MA (2022) Multi-task deep learning for glaucoma detection from color fundus images. Sci Rep 12:12361. https://doi.org/10.1038/s41598-022-16262-8

    Article  Google Scholar 

  48. Pachade S, Porwal P, Thulkar D, Kokare M, Deshmukh G, Sahasrabuddhe V, Giancardo L, Quellec G, Mériaudeau F (2021) Retinal fundus multi-disease image dataset (RFMiD): a dataset for multi-disease detection research. Data 6:14. https://doi.org/10.3390/data6020014

    Article  Google Scholar 

  49. Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. http://arxiv.org/abs/1502.03167

  50. Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15:1929–1958

    MATH  MathSciNet  Google Scholar 

  51. Agarap AF (2018) Deep learning using rectified linear units (ReLU). http://arxiv.org/abs/1803.08375

  52. Nwankpa C, Ijomah W, Gachagan A, Marshall S (2018) Activation functions: comparison of trends in practice and research for deep learning. http://arxiv.org/abs/1811.03378

  53. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. http://arxiv.org/abs/1409.1556

  54. Krizhevsky A, Sutskever I, Hinton GE (2017) ImageNet classification with deep convolutional neural networks. Commun ACM 60:84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  55. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2016) Inception-v4, inception-ResNet and the impact of residual connections on learning. http://arxiv.org/abs/1602.07261

  56. Yu X, Kang C, Guttery DS, Kadry S, Chen Y, Zhang Y-D (2020) ResNet-SCDA-50 for breast abnormality classification. IEEE/ACM Trans Comput Biol Bioinforma. 18:94–102. https://doi.org/10.1109/tcbb.2020.2986544

    Article  Google Scholar 

  57. Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S, Uszkoreit J (2020) An image is worth 16x16 words: transformers for image recognition at scale. http://arxiv.org/abs/2010.11929

Download references

Author information

Authors and Affiliations

  1. Centre for Advanced Studies, Dr. A. P. J. Abdul Kalam Technical University, Lucknow, Uttar Pradesh, India

    Neha Sengar, Rakesh Chandra Joshi & Malay Kishore Dutta

  2. BRNO University of Technology, Brno, Czech Republic

    Radim Burget

Authors
  1. Neha Sengar
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Rakesh Chandra Joshi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Malay Kishore Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Radim Burget
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Malay Kishore Dutta.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sengar, N., Joshi, R.C., Dutta, M.K. et al. EyeDeep-Net: a multi-class diagnosis of retinal diseases using deep neural network. Neural Comput & Applic 35, 10551–10571 (2023). https://doi.org/10.1007/s00521-023-08249-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-023-08249-x

Keywords

Search

Navigation