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Automatic detection of papilledema through fundus retinal images using deep learning

Saba, T, Akbar, S, Kolivand, H and Ali Bahaj, S (2021) Automatic detection of papilledema through fundus retinal images using deep learning. Microscopy Research and Technique, 84 (12). pp. 3066-3077. ISSN 1097-0029

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Abstract

Papilledema is a syndrome of the retina in which retinal optic nerve is inflated by elevation of intracranial pressure. The papilledema abnormalities such as retinal nerve fiber layer (RNFL) opacification may lead to blindness. These abnormalities could be seen through capturing of retinal images by means of fundus camera. This paper presents a deep learning-based automated system that detects and grades the papilledema through U-Net and Dense-Net architectures. The proposed approach has two main stages. First, optic disc and its surrounding area in fundus retinal image are localized and cropped for input to Dense-Net which classifies the optic disc as papilledema or normal. Second, consists of preprocessing of Dense-Net classified papilledema fundus image by Gabor filter. The preprocessed papilledema image is input to U-Net to achieve the segmented vascular network from which the Vessel Discontinuity Index (VDI) and Vessel Discontinuity Index to disc proximity (VDIP) are calculated for grading of papilledema. The VDI and VDIP are standard parameter to check the severity and grading of papilledema. The proposed system is evaluated on 60 papilledema and 40 normal fundus images taken from STARE dataset. The experimental results for classification of papilledema through Dense-Net are much better in terms of sensitivity 98.63%, specificity 97.83%, accuracy 99.17%. Similarly, the grading results for mild and severe papilledema classification through U-Net are also much better in terms of sensitivity 99.82%, specificity 98.65%, and accuracy 99.89%. The deep learning-based automated detection and grading of papilledema for clinical purposes is first effort in state of art.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Saba, T., Akbar, S., Kolivand, H., & Ali Bahaj, S. (2021). Automatic detection of papilledema through fundus retinal images using deep learning. Microscopy Research and Technique, 84( 12), 3066– 3077. https://doi.org/10.1002/jemt.23865, which has been published in final form at [Link to final article using the DOI]. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited.
Uncontrolled Keywords: 0299 Other Physical Sciences, 0601 Biochemistry and Cell Biology, 0912 Materials Engineering
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Computer Science & Mathematics
Publisher: Wiley
Related URLs:
Date Deposited: 06 Apr 2022 10:29
Last Modified: 06 Apr 2022 10:30
DOI or Identification number: 10.1002/jemt.23865
URI: https://researchonline.ljmu.ac.uk/id/eprint/16598

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