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Blood vessel segmentation in retinal fundus images for proliferative diabetic retinopathy screening using deep learning

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Abstract

Diabetic retinopathy (DR) is also called diabetic eye disease, which causes damage to the retina due to diabetes mellitus and that leads to blindness when the disease reaches an extreme stage. The medical tests take a lot of procedure, time, and money to test for the proliferative stage of diabetic retinopathy (PDR). Hence to resolve this problem, this model is proposed to detect and identify the proliferative stages of diabetic retinopathy which is also identified by its hallmark feature that is neovascularization. In the proposed system, the paper aims to correctly identify the presence of neovascularization using color fundus images. The presence of neovascularization in an eye is an indication that the eye is affected with proliferative PDR. Neovascularization is the development of new abnormal blood vessels in the retina. Since the occurrence of neovascularization may lead to partial or complete vision loss, timely and accurate prediction is important. The aim of the paper is to propose a method to detect the presence of neovascularization which involves image processing methods such as resizing, green channel filtering, Gaussian filter, and morphology techniques such as erosion and dilation. For classification, the different layers of CNN have been used and modeled together in a VGG-16 net architecture. The model was trained and tested on 2200 images all together from the Kaggle database. The proposed model was tested using DRIVE and STARE data sets, and the accuracy, specificity, sensitivity, precision, F1 score achieved are 0.96, 0.99, 0.95, 0.99, and 0.97, respectively, on DRIVE and 0.95, 0.99, 0.9375, 0.96, and 0.95, respectively, on STARE.

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References

  1. Yu, S., Xiao, D., Kanagasingam, Y.: Machine learning based automatic neovascularization detection on optic disc region. IEEE J. Biomed. Health Inform. 22(3), 886–894 (2018)

    Article  Google Scholar 

  2. Guo, X., Lu, X., Liu, Q., Che, X.: EMFN: enhanced multi-feature fusion network for hard exudate detection in fundus images. IEEE Access 7, 176912–176920 (2019)

    Article  Google Scholar 

  3. Qummar, S., Khan, F.G., Shah, S., Khan, A., Shamshirband, S., Rehman, Z.U., Khan, I.A., Jadoon, W.: A deep learning ensemble approach for diabetic retinopathy detection. IEEE Access 7, 150530–150539 (2019)

    Article  Google Scholar 

  4. Kar, S.S., Maity, S.P.: Gradation of diabetic retinopathy on reconstructed image using compressed sensing. IET Image Proc. 12(11), 1956–1963 (2018)

    Article  Google Scholar 

  5. Pan, J., Chen, D., Yang, X., et al.: Characteristics of neovascularization in early stages of proliferative diabetic retinopathy by optical coherence tomography angiography. Am J Ophthalmol. 192, 146–156 (2018)

    Article  Google Scholar 

  6. Lagae, A., Lefebvre, S., Dutre, P.: Improving gabor noise. IEEE Trans. Visual. Comput. Graph. 17(8), 1096–1107 (2011)

    Article  Google Scholar 

  7. Chang, Y., Jung, C., Ke, P., Song, H., Hwang, J.: Automatic contrast-limited adaptive histogram equalization with dual gamma correction. IEEE Access 6, 11782–11792 (2018)

    Article  Google Scholar 

  8. Liu, X., Chi, M., Zhang, Y., Qin, Y.: Classifying high resolution remote sensing images by fine-tuned VGG deep networks. In: IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, pp. 7137–7140 (2018)

  9. van Ginneken, B., Kerkstra, S., Meakin J.: DRIVE: Digital retinal images for vessel extraction. Available at https://drive.grand-challenge.org. Accessed 2012

  10. Hoover, A., Kouznetsova V., Goldbaum, M.: Structured analysis of the retina. Available at https://cecas.clemson.edu/~ahoover/stare/probing/index.html. Accessed 2000

  11. Thangaraj, S., Periyasamy, V., Balaji, R.: Retinal vessel segmentation using neural network. IET Image Proc. 12(5), 669–678 (2018)

    Article  Google Scholar 

  12. Lin, Y., Zhang, H., Hu, G.: Automatic retinal vessel segmentation via deeply supervised and smoothly regularized network. IEEE Access 7, 57717–57724 (2019)

    Article  Google Scholar 

  13. Remeseiro, B., Mendonça, A.M., Campilho, A: Automatic classification of retinal blood vessels based on multilevel thresholding and graph propagation. .Vis Comput. (2020)

  14. Shah, S.A.A., Shahzad, A., Khan, M.A., Lu, C., Tang, T.B.: Unsupervised method for retinal vessel segmentation based on gabor wavelet and multiscale line detector. IEEE Access 7, 167221–167228 (2019)

    Article  Google Scholar 

  15. Soomro, T.A., et al.: Impact of image enhancement technique on CNN model for retinal blood vessels segmentation. IEEE Access 7, 158183–158197 (2019)

    Article  Google Scholar 

  16. Javidi, M., Pourreza, H.R., Harati, A.: Vessel segmentation and microaneurysm detection using discriminative dictionary learning and sparse representation. Comput Methods Progr. Biomed. 139, 93–108 (2017)

    Article  Google Scholar 

  17. Li, X., Huang, H., Zhao, H., et al.: Learning a convolutional neural network for propagation-based stereo image segmentation. Vis. Comput. 36, 39–52 (2020)

    Article  Google Scholar 

  18. Hammad, I., El-Sankary, K.: Impact of approximate multipliers on VGG deep learning network. IEEE Access 6, 60438–60444 (2018)

    Article  Google Scholar 

  19. Chen, L., Wang, R., Yang, J., et al.: Multi-label image classification with recurrently learning semantic dependencies. Vis. Comput. 35, 1361–1371 (2019)

    Article  Google Scholar 

  20. Xiuqin, P., Zhang, Q., Zhang, H., Li, S.: A fundus retinal vessels segmentation scheme based on the improved deep learning U-net model. IEEE Access 7, 122634–122643 (2019)

    Article  Google Scholar 

  21. Yan, Z., Yang, X., Cheng, K.: A three-stage deep learning model for accurate retinal vessel segmentation. IEEE J. Biomed. Health Inform. 23(4), 1427–1436 (2019)

    Article  Google Scholar 

  22. Dharmawan, D.A., Li, D., Ng, B.P., Rahardja, S.: A new hybrid algorithm for retinal vessels segmentation on fundus images. IEEE Access. 7, 41885–41896 (2019)

    Article  Google Scholar 

  23. Cherukuri, V., Kumar, V., Bala, R., Monga, V.: Deep retinal image segmentation with regularization under geometric priors. IEEE Trans. Image Process. 29, 2552–2567 (2020)

    Article  Google Scholar 

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Acknowledgements

The authors would like to thank the SRM Institute of Science and Technology, Department of CSE, for providing an excellent atmosphere for researching on this topic.

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The authors received no specific funding for this research.

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  1. Department of Computer Science and Engineering, SRM Institute of Science and Technology, Chennai, 603203, India

    P. Saranya, S. Prabakaran, Rahul Kumar & Eshani Das

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  1. P. Saranya
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  2. S. Prabakaran
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  3. Rahul Kumar
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  4. Eshani Das
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Correspondence to P. Saranya.

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Saranya, P., Prabakaran, S., Kumar, R. et al. Blood vessel segmentation in retinal fundus images for proliferative diabetic retinopathy screening using deep learning. Vis Comput 38, 977–992 (2022). https://doi.org/10.1007/s00371-021-02062-0

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