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Proceedings Paper

Diagnosis of corneal pathologies using deep learning
Author(s): Amr Elsawy; Mohamed Abdel-Mottaleb; Mohamed Abou Shousha
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Paper Abstract

Corneal pathologies are leading causes of blindness and represent a world health problem according to the world health organization. Early detection of corneal diseases is necessary to prevent blindness. In this paper, we use transfer learning with pretrained deep learning networks to diagnose three common corneal diseases, namely, dry eye, Fuchs' endothelial dystrophy, and keratoconus as well as healthy eyes using only optical coherence tomography (OCT) images. Corneal OCT scans were obtained from 413 eyes of 269 patients and used to train, validate, and test the networks. All networks achieved all-category accuracy values > 99%, categorical area under curve values > 0:99, categorical specificity values > 99%, and categorical sensitivity values > 99% on the training, validation, and testing, respectively. The work in this paper has clinical significance and can potentially be applied in clinical practice to potentially solve a significant world health problem.

Paper Details

Date Published: 19 February 2020
PDF: 11 pages
Proc. SPIE 11218, Ophthalmic Technologies XXX, 1121828 (19 February 2020);
Show Author Affiliations
Amr Elsawy, Univ. of Miami (United States)
Mohamed Abdel-Mottaleb, Bascom Palmer Eye Institute (United States)
Mohamed Abou Shousha, Univ. of Miami (United States)
Bascom Palmer Eye Institute (United States)


Published in SPIE Proceedings Vol. 11218:
Ophthalmic Technologies XXX
Fabrice Manns; Arthur Ho; Per G. Söderberg, Editor(s)

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