Predicting retinal pathologies with IoMT-enabled hybrid ensemble deep network model-Reference-Cited by-同舟云学术

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Predicting retinal pathologies with IoMT-enabled hybrid ensemble deep network model

Published:2023-08-18 Issue:8 Volume:17 Page:4255-4264
ISSN:1863-1703
Container-title:Signal, Image and Video Processing
language:en
Short-container-title:SIViP

Author:

Priya J. Sathya,Angayarkanni S. A.,Balakiruthiga B.,Lalitha K.

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s11760-023-02658-0.pdf

Reference17 articles.

1. Toğaçar, M., Ergen, B., Tümen, V.: Use of dominant activations obtained by processing OCT images with the CNNs and slime mold method in retinal disease detection. Biocybern. Biomed. Eng. 42(2), 646–666 (2022)

2. Vishnu, S., Ramson, S.J., Jegan, R.: Internet of medical things (IoMT)-an overview. In: 2020 5th International Conference on Devices, Circuits, and Systems (ICDCS), pp. 101–104. IEEE (2020)

3. Kaothanthong, N., Limwattanayingyong, J., Silpa-archa, S., Tadarati, M., Amphornphruet, A., Singhanetr, P., Lalitwongsa, P., Chantangphol, P., Amornpetchsathaporn, A., Chainakul, M., Ruamviboonsuk, P.: The classification of common macular diseases using deep learning on optical coherence tomography images with and without prior automated segmentation. Diagnostics 13(2), 189 (2023)

4. Rajagopalan, N., Narasimhan, V., Kunnavakkam Vinjimoor, S., Aiyer, J.: Deep CNN framework for retinal disease diagnosis using optical coherence tomography images. J. Ambient Intell. Humaniz. Comput. 12, 7569–7580 (2021)

5. Sunija, A.P., Kar, S., Gayathri, S., Gopi, V.P., Palanisamy, P.: Octnet: a lightweight cnn for retinal disease classification from optical coherence tomography images. Comput. Methods Programs Biomed. 200, 105877 (2021)

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