Triwijoyo, Bambang Krismono and Sabarguna, Boy Subiroso and Budiharto, Widodo and Abdurachman, Edi (2020) RDCNET : CONVOLUTIONAL NEURAL NETWORKS FOR CLASSIFICATION OF RETINOPATHY DISEASE IN UNBALANCED DATA CASES. ICIC Express Letters, 14 (7). ISSN 1881-803X
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Abstract
Retinopathy disease is a type of retinal disorder, which often occurs, including hypertensive retinopathy and diabetic hypertension. Detection of retinopathy can be by analyzing the retinal image, using a deep learning approach, but the problem that is often faced is unbalanced data. In this study, a convolutional neural network architecture is proposed for the classification of retinopathy using the MESSIDOR database that has been labeled, by duplicating and augmentation of sample images in classes with low numbers of samples using a data generator to overcome the problem of unbalanced data. The experimental results show that the validation and testing accuracy performance on the model with two output classes are 100%, and 87.50%, while on the model with four output classes are 99.38%, and 76.47%
| Item Type: | Article |
|---|---|
| Keywords: | Deep learning, Convolutional neural network, Retinopathy diseases, Image classification, Unbalanced data |
| Subjects: | T Technology > T Technology (General) |
| Depositing User: | Bambang Krismono Triwijoyo |
| Date Deposited: | 20 Apr 2023 05:29 |
| Last Modified: | 20 Apr 2023 05:29 |
| URI: | http://repository.universitasbumigora.ac.id/id/eprint/2477 |
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