Diagnosis of Eye Retinal Diseases Based on Convolutional Neural Networks Using Optical Coherence Images

被引：20

作者：

Sertkaya, Mehmet Emre ^{[1
]}

Ergen, Burhan ^{[1
]}

Togacar, Mesut ^{[2
]}

机构：

[1] Firat Univ, Comp Engn, Fac Engn, Elazig, Turkey

[2] Firat Univ, Tech Vocat High Sch, Comp Technol Program, Elazig, Turkey

来源：

PROCEEDINGS OF THE 2019 23RD INTERNATIONAL CONFERENCE ELECTRONICS (ELECTRONICS 2019) | 2019年

关键词：

Biomedical optical imaging; Convolutional neural networks; Medical diagnosis; Supervised learning; MACULAR DEGENERATION; VISUAL-ACUITY; PATHOGENESIS; TOMOGRAPHY; THICKNESS;

D O I：

10.1109/electronics.2019.8765579

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this study, the diagnosis of some diseases in the retina of the eye by using deep learning architectures is intended to be diagnosed. Optical Coherence Tomography device from Choroidal Neovascularization, Diabetic Macular Edema, Drusen and healthy eye retinal images were examined. LeNet, AlexNet and Vgg16 architectures of deep learning were used. In each architecture, the hyper parameters were changed to diagnose these diseases. Results of the implementation showed that exhibit successful results in Vgg16 and AlexNet architecture. Dropout layer structure in AlexNet has been shown to reduce the loss by minimizing loss.

引用

页数：5

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