TY - GEN
T1 - Residual convolutional neural network for diabetic retinopathy
AU - Rufaida, Syahidahizza
AU - Fanany, Mohamad Ivan
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - This research proposes a method to detect diabetic retinopathy automatically based on fundus photography evaluation. This automatic method will speed up diabetic retinopathy detection process especially in Indonesia which lack of ophthalmologist. Besides, the difference of doctor ability and experience may produce an inconsistent result. Thus, with this method, we hope automatic detection of diabetic retinopathy will speed up with a consistent result so blindness effect from diabetic retinopathy can be prevented as early as possible. Convolutional Neural Network (CNN) is one of neural network variant which can detect the pattern on an image very well. Residual CNN is one of CNN variant which can prevent accuracy degradation for a deep neural network. Therefore this inspire us to apply Residual CNN on diabetic retinopathy. This Residual Network can detect diabetic retinopathy with kappa score 0.51049.
AB - This research proposes a method to detect diabetic retinopathy automatically based on fundus photography evaluation. This automatic method will speed up diabetic retinopathy detection process especially in Indonesia which lack of ophthalmologist. Besides, the difference of doctor ability and experience may produce an inconsistent result. Thus, with this method, we hope automatic detection of diabetic retinopathy will speed up with a consistent result so blindness effect from diabetic retinopathy can be prevented as early as possible. Convolutional Neural Network (CNN) is one of neural network variant which can detect the pattern on an image very well. Residual CNN is one of CNN variant which can prevent accuracy degradation for a deep neural network. Therefore this inspire us to apply Residual CNN on diabetic retinopathy. This Residual Network can detect diabetic retinopathy with kappa score 0.51049.
KW - Classification
KW - Differentiable Neural Computer
KW - Neural Network
KW - Sequence
UR - http://www.scopus.com/inward/record.url?scp=85051140776&partnerID=8YFLogxK
U2 - 10.1109/ICACSIS.2017.8355060
DO - 10.1109/ICACSIS.2017.8355060
M3 - Conference contribution
AN - SCOPUS:85051140776
T3 - 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
SP - 367
EP - 373
BT - 2017 International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th International Conference on Advanced Computer Science and Information Systems, ICACSIS 2017
Y2 - 28 October 2017 through 29 October 2017
ER -
