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RDCNET: CONVOLUTIONAL NEURAL NETWORKS FOR CLASSIFICATION OF RETINOPATHY DISEASE IN UNBALANCED DATA CASES
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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%.
Bambang Krismono Triwijoyo - Personal Name
Boy Subirosa Sabarguna - Personal Name
Widodo Budiharto - Personal Name
Edi Abdurachman - Personal Name
Boy Subirosa Sabarguna - Personal Name
Widodo Budiharto - Personal Name
Edi Abdurachman - Personal Name
1881-803X
NONE
electronic file
English
ICIC Express Letters
2020
Japan
Vol. 14, No. 7, Jil. 14, Terbitan 7, Hlm. 635-641
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