Publikasi Scopus FKUI 2021 per tanggal 30 Juni 2021 (428 artikel)

Bustamam A., Sarwinda D., Paradisa R.H., Victor A.A., Yudantha A.R., Siswantining T.
36815737800;56119401500;57221562575;57191055282;55489644900;57193446800;
Evaluation of convolutional neural network variants for diagnosis of diabetic retinopathy
2021
Communications in Mathematical Biology and Neuroscience
2021
42
Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok, Indonesia; Department of Ophthalmology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia
Bustamam, A., Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok, Indonesia; Sarwinda, D., Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok, Indonesia; Paradisa, R.H., Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok, Indonesia; Victor, A.A., Department of Ophthalmology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia; Yudantha, A.R., Department of Ophthalmology, Faculty of Medicine, Universitas Indonesia, Cipto Mangunkusumo National General Hospital, Jakarta, Indonesia; Siswantining, T., Department of Mathematics, Faculty of Mathematics and Natural Science, Universitas Indonesia, Depok, Indonesia
Diabetic Retinopathy (DR) is a long-term complication of Diabetes Mellitus (DM) that impairs vision. This stage occurs in visual impairment and blindness if treated late. DR identified through scanning fundus images. A technique on classifying DR in fundus images is the deep learning approach, one of the methods of implementing machine learning. In this study, the Convolutional Neural Networks (CNN) method applied with the ResNet-50 and DenseNet-121 architectures. The data adopted in this analysis was generated from DIARETDB1, an online database containing fundus images. Then, the pre-processing stage is carried out on the fundus image to improve model performance, such as selected the green channel from the images and inverted it, converted the images into grayscale images, and applied Contrast Limited Adaptive Histogram Equalization (CLAHE) for uniform contrast in the images. The outcome of this research indicates that the ResNet-50 model is better than DenseNet-121 in detecting DR. The most reliable results from the ResNet-50 model's case testing are accuracy, precision, and recall of 95%, 98%, and 96% respectively. © 2021, SCIK Publishing Corporation. All rights reserved.
Deep learning; Densenet; Diabetic retinopathy; Fundus image; Resnet
SCIK Publishing Corporation
20522541
Article
Q3
309
14099