Abstract

We propose a new method for training convolutional neural networks (CNNs) and use it to classify glaucoma from fundus images. This method integrates reinforcement learning along with supervised learning and uses it for transfer learning. The training method uses hill climbing techniques via two different climber types, namely, “random movement” and “random detection,” integrated with a supervised learning model through a stochastic gradient descent with momentum model. The model was trained and tested using the Drishti-GS and RIM-ONE-r2 datasets having glaucomatous and normal fundus images. The performance for prediction was tested by transfer learning on five CNN architectures, namely, GoogLeNet, DenseNet-201, NASNet, VGG-19, and Inception-Resnet v2. A five-fold classification was used for evaluating the performance, and high sensitivities while maintaining high accuracies were achieved. Of the models tested, the DenseNet-201 architecture performed the best in terms of sensitivity and area under the curve. This method of training allows transfer learning on small datasets and can be applied for tele-ophthalmology applications including training with local datasets.

© 2021 Optical Society of America

Full Article  |  PDF Article
More Like This
Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Juan J. Gómez-Valverde, Alfonso Antón, Gianluca Fatti, Bart Liefers, Alejandra Herranz, Andrés Santos, Clara I. Sánchez, and María J. Ledesma-Carbayo
Biomed. Opt. Express 10(2) 892-913 (2019)

Graph convolutional network based optic disc and cup segmentation on fundus images

Zhiqiang Tian, Yaoyue Zheng, Xiaojian Li, Shaoyi Du, and Xiayu Xu
Biomed. Opt. Express 11(6) 3043-3057 (2020)

Weakly supervised individual ganglion cell segmentation from adaptive optics OCT images for glaucomatous damage assessment

Somayyeh Soltanian-Zadeh, Kazuhiro Kurokawa, Zhuolin Liu, Furu Zhang, Osamah Saeedi, Daniel X. Hammer, Donald T. Miller, and Sina Farsiu
Optica 8(5) 642-651 (2021)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Data Availability

Data underlying the results presented in this paper are not publicly available at this time but may be obtained from the authors upon reasonable request.

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Figures (7)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Tables (7)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription

Metrics

You do not have subscription access to this journal. Article level metrics are available to subscribers only. You may subscribe either as an OSA member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access OSA Member Subscription