TY - GEN
T1 - Classification of Diabetic Retinopathy using shallow learning approach
AU - Pansawira, S.
AU - Bustamam, A.
AU - Sarwinda, D.
N1 - Publisher Copyright:
© 2020 Author(s).
PY - 2020/6/1
Y1 - 2020/6/1
N2 - Diabetic retinopathy (DR) is one of the leading causes of blindness from diabetic patients. To prevent blindness and provide an effective treatment, an early detection of DR is needed. Methods for detecting DR by manual inspections exist, but very time-consuming and tedious work. In this study, DR detection method is proposed, by using Shallow Learning approach that consists of Neural Networks, Support Vector Machine, and Random Forest. The data used to build the classifier models are DR class, Age-related Macular Degeneration (AMD) class, and Normal class. From experimental results, classification approach using Support Vector Machine yielded better results compared to Random Forest and Neural Networks. On multi-class DR, Normal, and AMD classification, Support Vector Machine method achieved 100 % accuracy and 100 % sensitivity on 75 % training data, and 94.87 % accuracy and 93.33 % sensitivity on 25 % testing data.
AB - Diabetic retinopathy (DR) is one of the leading causes of blindness from diabetic patients. To prevent blindness and provide an effective treatment, an early detection of DR is needed. Methods for detecting DR by manual inspections exist, but very time-consuming and tedious work. In this study, DR detection method is proposed, by using Shallow Learning approach that consists of Neural Networks, Support Vector Machine, and Random Forest. The data used to build the classifier models are DR class, Age-related Macular Degeneration (AMD) class, and Normal class. From experimental results, classification approach using Support Vector Machine yielded better results compared to Random Forest and Neural Networks. On multi-class DR, Normal, and AMD classification, Support Vector Machine method achieved 100 % accuracy and 100 % sensitivity on 75 % training data, and 94.87 % accuracy and 93.33 % sensitivity on 25 % testing data.
KW - Diabetic retinopathy
KW - neural networks
KW - random forest
KW - support vector machines
UR - http://www.scopus.com/inward/record.url?scp=85086676719&partnerID=8YFLogxK
U2 - 10.1063/5.0007881
DO - 10.1063/5.0007881
M3 - Conference contribution
AN - SCOPUS:85086676719
T3 - AIP Conference Proceedings
BT - Proceedings of the 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
A2 - Mart, Terry
A2 - Triyono, Djoko
A2 - Ivandini, Tribidasari Anggraningrum
PB - American Institute of Physics Inc.
T2 - 5th International Symposium on Current Progress in Mathematics and Sciences, ISCPMS 2019
Y2 - 9 July 2019 through 10 July 2019
ER -
