TY - GEN
T1 - Fuzzy learning vector quantization, neural network and fuzzy systems for classification fundus eye images with wavelet transformation
AU - Damayanti, Auli
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - The human eye is a complex organ that is essential for everyday life. The fundus is the inner surface of the eye, which lies contrary to the lens. The results of eye fundus shooting can be used to diagnose abnormalities that occur in the eye. Artificial neural networks and fuzzy systems are methods that can be used in the classification process. In this research used Levenberg-Marquardt (LM), adaptive neuro-fuzzy inference system (ANFIS), and fuzzy learning vector quantization (FLVQ) method in ANFIS clustering process for classification of retinal abdominal eye disease, Age-Related Macular Degeneration, and normal, with an input of energy coefficient, resulting from wavelet transformation process. From the results of the percentage of success of the system in the classification of disease in the eye fundus image, it appears that the system has been able to recognize the image pattern well, that is for ANFIS with lr = 0.4, mc = 0.9 is 100%, for ANFIS-FLVQ with lr = 0.9, mc = 0.1 is 100% and for LM with μ = 0.01 is 100%.
AB - The human eye is a complex organ that is essential for everyday life. The fundus is the inner surface of the eye, which lies contrary to the lens. The results of eye fundus shooting can be used to diagnose abnormalities that occur in the eye. Artificial neural networks and fuzzy systems are methods that can be used in the classification process. In this research used Levenberg-Marquardt (LM), adaptive neuro-fuzzy inference system (ANFIS), and fuzzy learning vector quantization (FLVQ) method in ANFIS clustering process for classification of retinal abdominal eye disease, Age-Related Macular Degeneration, and normal, with an input of energy coefficient, resulting from wavelet transformation process. From the results of the percentage of success of the system in the classification of disease in the eye fundus image, it appears that the system has been able to recognize the image pattern well, that is for ANFIS with lr = 0.4, mc = 0.9 is 100%, for ANFIS-FLVQ with lr = 0.9, mc = 0.1 is 100% and for LM with μ = 0.01 is 100%.
KW - ANFIS
KW - FLVQ
KW - LM
KW - eye fundus image
KW - wavelet transformation
UR - http://www.scopus.com/inward/record.url?scp=85050384737&partnerID=8YFLogxK
U2 - 10.1109/ICITISEE.2017.8285522
DO - 10.1109/ICITISEE.2017.8285522
M3 - Conference contribution
AN - SCOPUS:85050384737
T3 - Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017
SP - 331
EP - 336
BT - Proceedings - 2017 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conferences on Information Technology, Information Systems and Electrical Engineering, ICITISEE 2017
Y2 - 1 November 2017 through 2 November 2017
ER -
