RetoNet: a deep learning architecture for automated retinal ailment detection

  • Lekha R NairEmail author


Researchers are trying to tap the immense potential of big data to revolutionize all aspects of societal activity and to assist in having well informed decisions. Healthcare being one such field where proper analytics of available big medical data can lead to early detection and treatment of many ailments. Machine learning played a significant role in the design of automated diagnostic systems and today we have deep learning models in this arena which are outperforming human expertise in terms of predictive accuracy. This paper proposes RetoNet, a convolutional neural network architecture, which is trained and optimized to detect retinal ailment from fundus images with pronounced accuracy and its performance is also proven to be superior to a transfer learning based model developed for the same. Deep learning based e-diagnostic system can be an accurate, cost effective and convenient solution for the shortage of expertise on demand in the healthcare field.


Deep learning ANN Convolutional neural network E-health Retinal disease detection 



  1. 1.
    ARIA online [Online]. Available: [Accessed 10 Septembel 2018]
  2. 2.
    Arunkumar R, Karthigaikumar P (2017) Multi-retinal disease classification by reduced deep learning features. Neural Comput & Applic 28(2):329–334CrossRefGoogle Scholar
  3. 3.
    Chen D, Mak BK-W (2015) Multitask learning of deep neural networks for low-resource speech recognition. IEEE/ACM Transactions on Audio, Speech and Language Processing 23(7):1172–1183Google Scholar
  4. 4.
    Cheng J, Wong DWK, Cheng X, Liu J, Tan NM, Bhargava M, Cheung CMG and Wong TY (2012) Early age-related macular degeneration detection by focal biologically inspired feature, in 19th IEEE International Conference on Image Processing (ICIP) Google Scholar
  5. 5.
    Chollet F (2015) Keras: Deep learning library for theano and tensorflow, URL: , vol. 7, no. 8
  6. 6.
    Das S, Malathy C (2018) Survey on diagnosis of diseases from retinal images. J Phys Conf Ser 1000(1):012053CrossRefGoogle Scholar
  7. 7.
    Dhoot DS, Baker K, Saroj N, Vitti R, Berliner AJ, Metzig C, Thompson D, Singh RP (2018) Baseline factors affecting changes in diabetic retinopathy severity scale score after intravitreal Aflibercept or laser for diabetic macular edema. Ophtalmology 125(1):51–56CrossRefGoogle Scholar
  8. 8.
    Duchi J, Hazan E, Singer Y (2011) Adaptive subgradient methods for online learning and stochastic optimization. J Mach Learn Res 12(Jul):2121–2159MathSciNetzbMATHGoogle Scholar
  9. 9.
    Faust O, Acharya R, Ng EY-K, Ng K-H, Suri JS (2012) Algorithms for the automated detection of diabetic retinopathy using digital fundus images: a review. J Med Syst 36(1):145–157CrossRefGoogle Scholar
  10. 10.
    Ferris I, Frederick L, Wilkinson CP, Bird A, Chakravarthy U, Chew E, Csaky K, Sadda SR, a. B. I. f. M. R. C. Committee (2013) Clinical classification of age-related macular degeneration. Ophthalmology 120(4):844–851CrossRefGoogle Scholar
  11. 11.
    Finger R, Fenwick E and Lamoureux E (2013) Impact of Early and Late Age-Related Macular Degeneration on Quality of Life, in Ophthalmology and the Ageing Society,Essentials in Ophthalmology, Berlin, SpringerGoogle Scholar
  12. 12.
    Fu H, Xu Y, Wong DWK and Liu J(2016) Retinal vessel segmentation via deep learning network and fully-connected conditional random fields, in 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI) Google Scholar
  13. 13.
    Geert L, Thijs K, Babak EB, Arnaud AAS (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88CrossRefGoogle Scholar
  14. 14.
    Goodfellow I, Bengio Y, Courville A, Bengio Y (2016) Deep learning. MIT press, CambridgezbMATHGoogle Scholar
  15. 15.
    Helbing D (2019) Societal, economic, ethical and legal challenges of the digital revolution: From big data to deep learning, artificial intelligence, and manipulative technologies, in Towards Digital Enlightenment, Springer, pp. 47–72Google Scholar
  16. 16.
    Hijazi MHA, Coenen F and Zheng AY, (2010) Retinal image classification using a histogram based approach, in The 2010 International Joint Conference on Neural Networks (IJCNN) Google Scholar
  17. 17.
    Khunger M, Choudhury T, Satapathy SC and Ting K-C (2019) Automated Detection of Glaucoma Using Image Processing Techniques, Emerging Technologies in Data Mining and Information Security, pp. 323–335Google Scholar
  18. 18.
    Kingma DP and Ba J, (2014) Adam: A method for stochastic optimization, arXiv preprint arXiv:1412.6980 Google Scholar
  19. 19.
    Kruger N, Janssen P, Kalkan S, Lappe M, Leonardis A, Piater J, Rodriguez-Sanchez AJ, Wiskott L (2013) Deep hierarchies in the primate visual cortex: what can we learn for computer vision? IEEE Trans Pattern Anal Mach Intell 35(8):1847–1871CrossRefGoogle Scholar
  20. 20.
    LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436CrossRefGoogle Scholar
  21. 21.
    Liu Y, Nie L, Liu L, Rosenblum DS (2016) From action to activity: sensor-based activity recognition. Neurocomputing 181:108–115CrossRefGoogle Scholar
  22. 22.
    Mendonca AM, Campilho A (2006) Segmentation of retinal blood vessels by combining the detection of centerlines and morphological reconstruction. IEEE Trans Med Imaging 25(9):1200–1213CrossRefGoogle Scholar
  23. 23.
    Nazari Khanamiri H, Nakatsuka A, El-Annan J (2017) Smartphone fundus photography. Journal of Visualized Experiments : JoVE 125(2017):55958Google Scholar
  24. 24.
    Niemeijer M, Ginneken BV, Russell SR, Suttorp-Schulten MS, Abramoff MD (2007) Automated detection and differentiation of drusen, exudates, and cotton-wool spots in digital color fundus photographs for diabetic retinopathy diagnosis. Invest Ophthalmol Vis Sci 48(5):2260–2267CrossRefGoogle Scholar
  25. 25.
    Rapantzikos K, Zervakis M, Balas K (2003) Detection and segmentation of drusen deposits on human retina: potential in the diagnosis of age-related macular degeneration. Med Image Anal 7(1):95–98CrossRefGoogle Scholar
  26. 26.
    Simonyan K and Zisserman A (2015) Very deep convolutional networks for large-scale image recognition., in International Conference on Learning Representations 2015 Google Scholar
  27. 27.
    Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. The Journal of Machine Learning Research 15(1):1929–1958MathSciNetzbMATHGoogle Scholar
  28. 28.
    Suykens JA, Vandewalle J Least squares support vector machine classifiers. Neural Process Lett 9(3):293–300Google Scholar
  29. 29.
    Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A (2015) Going deeper with convolutions, in IEEE conference on computer vision and pattern recognition Google Scholar
  30. 30.
    Tieleman T, Hinton G (2012) Lecture 6.5-rmsprop: divide the gradient by a running average of its recent magnitude. COURSERA: Neural networks for machine learning 4(2):26–31Google Scholar
  31. 31.
    Tufail A, Rudisill C, Egan C, Kapetanakis VV, Salas-Vega S, Owen CG, Lee A (2017) Automated diabetic retinopathy image assessment software: diagnostic accuracy and cost-effectiveness compared with human graders. Ophthalmology 124(3):343–351CrossRefGoogle Scholar
  32. 32.
    Wang S-H, Cheng H, Phillips P, Zhang Y-D (2018) Multiple sclerosis identification based on fractional Fourier entropy and a modified Jaya algorithm. Entropy 20(4):254CrossRefGoogle Scholar
  33. 33.
    Zheng Y, Hijazi MHA, Coenen F (2012) Automated “disease/no disease” grading of age-related macular degeneration by an image mining approach. Invest Ophthalmol Vis Sci 53(12):8310–8318CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.College of EngineeringKalloopparaIndia

Personalised recommendations