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Article

Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images

1
Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok 10520, Thailand
2
School of Information and Telecommunication Engineering, Tokai University, Tokyo 108–8619, Japan
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(14), 4916; https://doi.org/10.3390/app10144916
Submission received: 10 June 2020 / Revised: 9 July 2020 / Accepted: 12 July 2020 / Published: 17 July 2020
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅱ)

Abstract

Glaucoma is a major global cause of blindness. As the symptoms of glaucoma appear, when the disease reaches an advanced stage, proper screening of glaucoma in the early stages is challenging. Therefore, regular glaucoma screening is essential and recommended. However, eye screening is currently subjective, time-consuming and labor-intensive and there are insufficient eye specialists available. We present an automatic two-stage glaucoma screening system to reduce the workload of ophthalmologists. The system first segmented the optic disc region using a DeepLabv3+ architecture but substituted the encoder module with multiple deep convolutional neural networks. For the classification stage, we used pretrained deep convolutional neural networks for three proposals (1) transfer learning and (2) learning the feature descriptors using support vector machine and (3) building ensemble of methods in (1) and (2). We evaluated our methods on five available datasets containing 2787 retinal images and found that the best option for optic disc segmentation is a combination of DeepLabv3+ and MobileNet. For glaucoma classification, an ensemble of methods performed better than the conventional methods for RIM-ONE, ORIGA, DRISHTI-GS1 and ACRIMA datasets with the accuracy of 97.37%, 90.00%, 86.84% and 99.53% and Area Under Curve (AUC) of 100%, 92.06%, 91.67% and 99.98%, respectively, and performed comparably with CUHKMED, the top team in REFUGE challenge, using REFUGE dataset with an accuracy of 95.59% and AUC of 95.10%.
Keywords: glaucoma; retinal images; optic disc segmentation; deep learning; DeepLabv3+; deep activated features; ensemble classifier; support vector machine glaucoma; retinal images; optic disc segmentation; deep learning; DeepLabv3+; deep activated features; ensemble classifier; support vector machine

Share and Cite

MDPI and ACS Style

Sreng, S.; Maneerat, N.; Hamamoto, K.; Win, K.Y. Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images. Appl. Sci. 2020, 10, 4916. https://doi.org/10.3390/app10144916

AMA Style

Sreng S, Maneerat N, Hamamoto K, Win KY. Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images. Applied Sciences. 2020; 10(14):4916. https://doi.org/10.3390/app10144916

Chicago/Turabian Style

Sreng, Syna, Noppadol Maneerat, Kazuhiko Hamamoto, and Khin Yadanar Win. 2020. "Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images" Applied Sciences 10, no. 14: 4916. https://doi.org/10.3390/app10144916

APA Style

Sreng, S., Maneerat, N., Hamamoto, K., & Win, K. Y. (2020). Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images. Applied Sciences, 10(14), 4916. https://doi.org/10.3390/app10144916

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