Classification of Diabetic Retinopathy and Retinal Vein Occlusion in Human Eye Fundus Images by Transfer Learning

  • Ali Usman
  • Aslam MuhammadEmail author
  • A. M. Martinez-Enriquez
  • Adrees Muhammad
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1130)


Sight threatening diseases are viral these days. Some of these are so harmful that it may cause complete vision loss. Diabetic Retinopathy (DR) and Retinal Vein Occlusion (RVO) are from this category. The first step to cure such diseases is to accurately predict it. For prediction of these diseases there is a large number of machine/deep learning algorithms employed. In this research, we have proposed a DR&RVO prediction system, which may help eye specialists for the prediction of these diseases. The proposed methodology shows that a retinal image undergoes through three main steps in a Deep Neural Network (DNN) like, preprocessing, image segmentation, and feature extraction and classification. For classification of this processed image into DR and RVO, and normal labels, pre-trained deep neural networks (DNNs) are used. More than 2680 eye fundus images are collected from 7 online available datasets, all images are converted to jpg file format during preprocessing step, after class labels distribution into three categories, the proposed model is firstly trained and then tested randomly on Inception v3, ResNet50 and Alex Net. This is done by using a deep learning technique named as ‘Transfer Learning’. The accuracy obtained from these models shows that Inception v3 (85.2%) outperformed than other two state of the art models.


Diabetic Retinopathy (DR) Retinal Vein Occlusion (RVO) Transfer Learning (TL) Deep Neural Networks (DNNs) 



This research is funded by the National Research Program for Universities (NRPU), Higher Education Commission (HEC), Islamabad, Pakistan, grant number 20-9649/Punjab/NRPU/R&D/HEC/2017-18.


  1. 1.
    Samant, P., Agarwal, R.: Machine learning techniques for medical diagnosis of diabetes using iris images. Comput. Methods Programs Biomed. 157, 121–128 (2018)CrossRefGoogle Scholar
  2. 2.
    Kaur, M., Talwar, R.: Automatic extraction of blood vessel and eye retinopathy detection. Eur. J. Adv. Eng. Technol. 2(4), 57–61 (2015)Google Scholar
  3. 3.
    Guo, J., et al.: Automatic retinal blood vessel segmentation based on multi-level convolutional neural network. In: 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE (2018)Google Scholar
  4. 4.
    Qureshi, I., et al.: Computer aided systems for diabetic retinopathy detection using digital fundus images: a survey. Curr. Med. Imaging Rev. 12(4), 234–241 (2016)CrossRefGoogle Scholar
  5. 5.
    Solkar, S.D., Das, L.: Survey on retinal blood vessels segmentation techniques for detection of diabetic retinopathy. Diabetes (2017) Google Scholar
  6. 6.
    Pratt, H., et al.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)CrossRefGoogle Scholar
  7. 7.
    Nicolò, M., et al.: Real-life management of patients with retinal vein occlusion using I-macula web platform. J. Ophthalmol. (2017)Google Scholar
  8. 8.
    Zode, J.J., Pranali, C.C.: Detection of branch retinal vein occlusions using fractal analysis. Asian J. Convergence Technol. (AJCT)-UGC LISTED 3 (2017)Google Scholar
  9. 9.
    Fazekas, Z., et al.: Influence of using different segmentation methods on the fractal properties of the identified retinal vascular networks in healthy retinas and in retinas with vein occlusion, pp. 361–373 (2015)Google Scholar
  10. 10.
    Schmidt-Erfurth, U., et al.: Artificial intelligence in retina. Prog. Retinal Eye Res. (2018)Google Scholar
  11. 11.
    Ramachandran, N., et al.: Diabetic retinopathy screening using deep neural network. Clin. Exp. Ophthalmol. 46(4), 412–416 (2018)Google Scholar
  12. 12.
    Roy, P., et al.: 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). IEEE (2017)Google Scholar
  13. 13.
    Dutta, S., et al.: Classification of diabetic retinopathy images by using deep learning models. Int. J. Grid Distrib. Comput. 11(1), 89–106 (2018)CrossRefGoogle Scholar
  14. 14.
    Padmanabha, A.G.A., et al.: Classification of diabetic retinopathy using textural features in retinal color fundus image. In: 2017 12th International Conference on Intelligent Systems and Knowledge Engineering (ISKE). IEEE (2017)Google Scholar
  15. 15.
    Annunziata, R., et al.: Leveraging multiscale hessian-based enhancement with a novel exudate inpainting technique for retinal vessel segmentation. IEEE J. Biomed. Health Informat. 20(4), 1129–1138 (2016)Google Scholar
  16. 16.
    Khomri, B., et al.: Retinal blood vessel segmentation using the elite-guided multi-objective artificial bee colony algorithm. IET Image Process. 12(12), 2163–2171 (2018)CrossRefGoogle Scholar
  17. 17.
    Saleh, E., et al.: Learning ensemble classifiers for diabetic retinopathy assessment. Artif. Intell. Med. 85, 50–63 (2018)CrossRefGoogle Scholar
  18. 18.
    Hassan, G., et al.: Retinal blood vessel segmentation approach based on mathematical morphology. Procedia Comput. Sci. 65, 612–622 (2015)CrossRefGoogle Scholar
  19. 19.
    Zaheer, R., Humera, S.: GPU-based empirical evaluation of activation functions in convolutional neural networks. In: 2018 2nd International Conference on Inventive Systems and Control (ICISC). IEEE (2018)Google Scholar
  20. 20.
    Szegedy, C., et al.: Rethinking the inception architecture for computer vision. arXiv preprint arXiv:1512.00567 (2018)
  21. 21.
    He, K., et al.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016)Google Scholar
  22. 22.
    Krizhevsky, A., Ilya, S., Geoffrey, E.H.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Ali Usman
    • 1
  • Aslam Muhammad
    • 1
    Email author
  • A. M. Martinez-Enriquez
    • 2
  • Adrees Muhammad
    • 3
  1. 1.Department of Computer Science and EngineeringUETLahorePakistan
  2. 2.Department of Computer ScienceCINVESTAVMexico CityMexico
  3. 3.Department of Computer ScienceSuperior UniversityLahorePakistan

Personalised recommendations