Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images
| dc.contributor.author | Sreng, Syna | |
| dc.contributor.author | Maneerat, Noppadol | |
| dc.contributor.author | Hamamoto, Kazuhiko | |
| dc.contributor.author | Win, Khin Yadanar | |
| dc.date.accessioned | 2026-08-06T10:29:07Z | |
| dc.date.available | 2026-08-06T10:29:07Z | |
| dc.date.issued | 2020-07-01 | |
| dc.description.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%. | |
| dc.identifier.citation | Applied Sciences Switzerland, 10(14), 2020 | |
| dc.identifier.doi | 10.3390/app10144916 | |
| dc.identifier.issn | 20763417 | |
| dc.identifier.other | 2-s2.0-85088641149 | |
| dc.identifier.uri | https://dspace.kmitl.ac.th/handle/123456789/11098 | |
| dc.source | Applied Sciences Switzerland | |
| dc.subject | Deep activated features | |
| dc.subject | Deep learning | |
| dc.subject | Deeplabv3+ | |
| dc.subject | Ensemble classifier | |
| dc.subject | Glaucoma | |
| dc.subject | Optic disc segmentation | |
| dc.subject | Retinal images | |
| dc.subject | Support vector machine | |
| dc.title | Deep learning for optic disc segmentation and glaucoma diagnosis on retinal images | |
| dc.type | Article |