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Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN

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Abstract

In this study, we intend to diagnose Choroidal Neovascularization in retinal Optical Coherence Tomography (OCT) images using Deep Learning Architectures. OCT images can be used to differentiate between a healthy eye and an eye with CNV disease. In CNV the Retinal Pigment Epithelial layer experiences changes in various properties which can be related to the assistance of OCT Images. This paper proposes a technique for finding CNV in OCTA pictures. Among the few attributes of CNV, the bigger turning point of veins is a moderately clear element, so we will utilize this property to see if there is CNV in an OCTA picture. DenseNet and Vgg16 Architectures of Deep Learning were used in the study and the hyper parameters of both of the architectures were changed to diagnose the disease properly. After the detection of the disease, the diseased OCT images are segmented from the background for the Region of Interest detection using Python OpenCV library that is used for the processing of images. The results of implementation of the Architectures showed that Vgg16 showed better results in detecting the images rather than Dense Net Architecture with an accuracy percentage of 97.53% approximately a percent greater than Dense Net.

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References

  1. Lee, C. S. (2017). Deep-learning based automated segmentation of macular edema in optical coherence tomography. Biomedical Optics Express, 8(7), 3440–3448.

    Article  Google Scholar 

  2. Scigel, T. (2018). Fully automated detection and quantification of macular fluid in OCT using deep learning. London: Elsevier.

    Google Scholar 

  3. Kwasigroch, A., Jarzembinski, B., & Grochowski, M. (2018). Deep CNN based decision support system for detection and assessing the stage of diabetic retinopathy. In International interdisciplinary Ph.D. workshop (IIPhDW), Swinoujście (pp. 111–116).

  4. Soomro, T. A., et al. (2019). Deep learning models for retinal blood vessels segmentation: A review. IEEE Access, 7, 71696–71717.

    Article  Google Scholar 

  5. Li, F., Chen, H., Liu, Z., Zhang, X.-D., Jiang, M.-S., Zhi-zheng, Wu., & Zhou, K.-Q. (2019). Deep Learning-based automated detection of retinal diseases using optical coherence tomography images. Biomed Optical Press, 10(12), 6204–6226.

    Article  Google Scholar 

  6. Kamran, S. A., Saha, S., Sabbir, A. S., & Tavakkoli, A. (2020). Optic-net: A novel convolutional neural network for diagnosis of retinal diseases from optical tomography images. In IEEE international conference on machine learning and applications.

  7. Ergen, B., & Sertkaya, N. (2019). Diagnosis of eye retinal disease based on convolutional neural networks using optical coherence images. In IEEE.

  8. Motozawa, N. (2019). Optical coherence tomography-based deep learning models for classifying normal and age related macular degeneration and exudative and non-exudative age related macular degeneration changes. Ophthalmology and Therapy, 8(4), 527–539.

    Article  Google Scholar 

  9. Amor, R. D., et al. (2019). Towards automatic glaucoma assessment: An encoder–decoder CNN for retinal layer segmentation in rodent OCT images. In: European signal processing conference (EUSIPCO), A Coruna, Spain.

  10. Huan, L. (2019). Automatic classification of retinal optical coherence tomography with layer guided convolutional neural networks. IEEE, 26, 1026–1030.

    Google Scholar 

  11. Sengar, N. (2018). Detection of diabetic macular edema in retinal images using region based method. In IEEE (pp. 412–415).

  12. Chen, X., et. al. (2019). Retinal optical coherence tomography image analysis. Biological and Medical Physics, and Biomedical Engineering. Springer, (pp. 116–139).

  13. Wang, J. (2018). Deep learning for quality assessment of retina OCT images (Vol. 10, pp. 6057–6072). New York: Biomedical Express Publishing, OSA.

    Google Scholar 

  14. Lee, C. (2017). Deep learning is effective for classifying normal versus age-related macular degeneration OCT images. Elsevier, 1(4), 322–327.

    Google Scholar 

  15. Lodhi, B., & Kang, J. (2019). Multipath-dense net: A supervised ensemble architecture of densely connected convolutional networks. Elsevier, 482, 63–72.

    Google Scholar 

  16. Saha, M. (2018). Transfer learning by using VGG16 and Alex net model. In IEEE (pp. 656–660).

  17. Mishra, S. S., Mandal, B., & Puhan, N. B. (2019). Multi-level dual-attention based cnn for macular optical coherence tomography classification. IEEE Signal Processing Letters, 26(12), 1793–1797.

    Article  Google Scholar 

  18. Ngo, L., & Han, J.-H. (2017). Advanced deep learning for blood vessel segmentation in retinal fundus images. In International winter conference on brain-computer interface (pp. 91–92).

  19. Kamran, S. A., Tavakkoli, A., & Zuckerbrod, S. L. (2020). Improving robustness using joint attention network for detecting retinal degeneration from optical coherence tomography images. In IEEE international conference on image processing.

  20. Dash, P., & Sigappi, A. N. (2018). Detection and classification of retinal diseases in spectral domain optical coherence tomography images based on surf despcriptors. In IEEE International conference on system, computation, automation and networking.

  21. Linhares, O., Mendes, C., et al. (2019). automatic segmentation of macular holes in optical coherence tomography images: A review. IEC Science, 1, 163–185.

    Google Scholar 

  22. Wang, H., Li, Z., Yang Li, B. B., & Gupta, C. C. (2020). Visual saliency guided complex image retrieval”. Pattern Recognition Letters, 130, 64–72. https://doi.org/10.1016/j.patrec.2018.08.010

    Article  Google Scholar 

  23. Li, D., Deng, L., Gupta, B. B., Wang, H., & Choi, C. (2019). A novel CNN based security guaranteed image watermarking generation scenario for smart city applications. Information Sciences, 479, 432–447. https://doi.org/10.1016/j.ins.2018.02.060

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Software Engineering, SRM Institute of Science and Technology, Kattankulathur, India

    M. S. Abirami, Sarthak Kawatra & Anuja Vaishnava

  2. Department of Mathematics, SRM Institute of Science and Technology, Kattankulathur, India

    B. Vennila

  3. Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, Kattankulathur, India

    K. Suganthi

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  1. M. S. Abirami
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  2. B. Vennila
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  3. K. Suganthi
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  4. Sarthak Kawatra
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  5. Anuja Vaishnava
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Correspondence to M. S. Abirami.

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Abirami, M.S., Vennila, B., Suganthi, K. et al. Detection of Choroidal Neovascularization (CNV) in Retina OCT Images Using VGG16 and DenseNet CNN. Wireless Pers Commun 127, 2569–2583 (2022). https://doi.org/10.1007/s11277-021-09086-8

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