Advertisement

A Comparison of Computer Vision Methods for the Combined Detection of Glaucoma, Diabetic Retinopathy and Cataracts

Conference paper
  • 166 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12722)

Abstract

This paper focuses on the accurate, combined detection of glaucoma, diabetic retinopathy, and cataracts, all using a single computer vision pipeline. Attempts have been made in past literature; however, they mainly focus on only one of the aforementioned eye diseases. These diseases must be identified in the early stages to prevent damage progression. Three pipelines were constructed, of which 12 deep learning models and 8 Support Vector Machines (SVM) classifiers were trained. Pipeline 1 extracted Histogram of Oriented Gradients (HOG) features, and pipeline 2 extracted Grey-Level Co-occurrence Matrix (GLCM) textural features from the pre-processed images. These features were classified with either a linear or Radial Basis Function (RBF) kernel SVM. Pipeline 3 utilised various deep learning architectures for feature extraction and classification. Two models were trained for each deep learning architecture and SVM classifier, using standard RGB images (labelled as Normal). The other uses retina images with only the green channel present (labelled as Green). The Inception V3 Normal model achieved the best performance with accuracy and an F1-Score of 99.39%. The SqueezeNet Green model was the worst-performing deep learning model with accuracy and an F1-Score of 81.36% and 81.29%, respectively. Although it performed the worst, the model size is 5.03 MB compared to the 225 MB model size of the top-performing Inception V3 model. A GLCM feature selection study was performed for both the linear and RBF SVM kernels. The RBF SVM that extracted HOG features on the green-channel images performed the best out of the SVMs with accuracy and F1-Score of 76.67% and 76.48%, respectively. The green-channel extraction was more effective on the SVM classifiers than the deep learning models. The Inception V3 Normal model can be integrated with a computer-aided system to facilitate examiners in detecting diabetic retinopathy, cataracts and glaucoma.

Keywords

Glaucoma Diabetic retinopathy Cataract Computer vision Convolutional neural network Deep learning GLCM HOG CAD 

References

  1. 1.
    Ettore Giardini, M.: The portable eye examination kit: mobile phones can screen for eye disease in low-resource settings. IEEE Pulse 6, 15–17 (2015)CrossRefGoogle Scholar
  2. 2.
    Kazi, A., Ajmera, M., Sukhija, P., Devadkar, K.: Processing retinal images to discover diseases. In: 2018 International Conference on Current Trends towards Converging Technologies (ICCTCT) (2018)Google Scholar
  3. 3.
    Labuschagne, M.: Glaucoma: what should the general practitioner know? S. Afr. Fam. Pract. 55, 134–141 (2013)CrossRefGoogle Scholar
  4. 4.
    Thomas, R., et al.: Incidence and progression of diabetic retinopathy within a private diabetes mellitus clinic in South Africa. J. Endocrinol. Metab. Diab. S. Afr. 20, 127–133 (2015)Google Scholar
  5. 5.
    Acharya, U., Dua, S., Du, X., Sree, S.V., Chua, C.: Automated diagnosis of glaucoma using texture and higher order spectra features. IEEE Trans. Inf. Technol. Biomed. 15, 449–455 (2011)Google Scholar
  6. 6.
    Guo, L., Yang, J., Peng, L., Li, J., Liang, Q.: A computer-aided healthcare system for cataract classification and grading based on fundus image analysis. Comput. Ind. 69, 72–80 (2015)CrossRefGoogle Scholar
  7. 7.
    Singh, T., Bharali, P., Bhuyan, C.: Automated detection of diabetic retinopathy. In: 2019 Second International Conference on Advanced Computational and Communication Paradigms (ICACCP) (2019)Google Scholar
  8. 8.
    Panse, N., Ghorpade, T., Jethani, V.: Retinal fundus diseases diagnosis using image mining. In: 2015 International Conference on Computer, Communication and Control (IC4) (2015)Google Scholar
  9. 9.
  10. 10.
    Xu, Y., MacGillivray, T., Trucco, E.: Computational Retinal Image Analysis. Elsevier Science & Technology (2019)Google Scholar
  11. 11.
    Imran, A., Li, J., Pei, Y., Akhtar, F., Yang, J., Wang, Q.: Cataract detection and grading with retinal images using SOM-RBF neural network. In: 2019 IEEE Symposium Series on Computational Intelligence (SSCI) (2019)Google Scholar
  12. 12.
    Jothi, A.K., Mohan, P.: A comparison between KNN and SVM for breast cancer diagnosis using GLCM shape and LBP features. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT) (2020)Google Scholar
  13. 13.
    Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2005) (2005)Google Scholar
  14. 14.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3, 610–621 (1973)CrossRefGoogle Scholar
  15. 15.
    Patro, V., Patra, M.: A novel approach to compute confusion matrix for classification of n-class attributes with feature selection. Trans. Mach. Learn. Artif. Intell. 3(2), 52 (2015)Google Scholar
  16. 16.
    Memari, N., Abdollahi, S., Ganzagh, M., Moghbel, M.: Computer-assisted diagnosis (CAD) system for diabetic retinopathy screening using color fundus images using deep learning. In: 2020 IEEE Student Conference on Research and Development (SCOReD) (2020)Google Scholar
  17. 17.
    Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)Google Scholar
  18. 18.
    Chollet, F.: Xception: deep learning with depthwise separable convolutions. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  19. 19.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60, 84–90 (2017)CrossRefGoogle Scholar
  20. 20.
    Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.: Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)Google Scholar
  21. 21.
    He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-46493-0_38CrossRefGoogle Scholar
  22. 22.
    Iandola, F., Han, S., Moskewicz, M., Ashraf, K., Dally, W., Keutzer, K.: SqueezeNet: AlexNet-level accuracy with 50× fewer parameters and <0.5MB model size, http://arxiv.org/abs/1602.07360

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  1. 1.University of JohannesburgJohannesburgSouth Africa

Personalised recommendations