An automated hybrid decoupled convolutional network for laceration segmentation and grading of retinal diseases using optical coherence tomography (OCT) images-Reference-Cited by-同舟云学术

An automated hybrid decoupled convolutional network for laceration segmentation and grading of retinal diseases using optical coherence tomography (OCT) images

Published:2024-01-13 Issue:3 Volume:18 Page:2903-2927
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Mani Pavithra^ORCID,Ramachandran Neelaveni^ORCID,Paul Sweety Jose^ORCID,Ramesh Prasanna Venkatesh^ORCID

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s11760-023-02959-4.pdf

Reference62 articles.

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2. Sabanayagam, C., Banu, R., Chee, M.L., Lee, R., Wang, Y.X., Tan, G., et al.: Incidence and progression of diabetic retinopathy: a systematic review. Lancet Diabetes Endocrinol. 7(2), 140–149 (2019). https://doi.org/10.1016/S22138587(18)30128-1

3. Hassan, B., Ahmed, R., Li, B., Noor, A., Hassan, Z.U.: A comprehensive study capturing vision loss burden in Pakistan (1990–2025): findings from the global burden of disease (GBD) 2017 study. PLoS ONE 14(5), e0216492 (2019). https://doi.org/10.1371/journal.pone.0216492

4. Das, S., Kharbanda, K., Suchetha, M., Raman, R., Dhas, E.: Deep learning architecture based on segmented fundus image features for classification of diabetic retinopathy. Biomed. Signal Process. Control 68, 102600 (2021). https://doi.org/10.1016/j.bspc.2021.102600

5. Hassan, T., Akram, M.U., Werghi, N.: Exploiting the transferability of deep learning systems across multi-modal retinal scans for extracting retinopathy laceration. In: 2020 IEEE 20th International Conference on Bioinformatics and Bioengineering (BIBE), pp. 577–581, IEEE (2020). https://doi.org/10.1109/BIBE50027.2020.00099

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