TY - GEN
T1 - Automated diagnosis system of diabetic retinopathy using GLCM method and SVM classifier
AU - Foeady, Ahmad Zoebad
AU - Novitasari, Dian Candra Rini
AU - Asyhar, Ahmad Hanif
AU - Firmansjah, Muhammad
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10
Y1 - 2018/10
N2 - Diabetic Retinopathy (DR) is the cause of blindness. Early identification needed for prevent the DR. However, High hospital cost for eye examination makes many patients allow the DR to spread and lead to blindness. This study identifies DR patients by using color fundus image with SVM classification method. The purpose of this study is to minimize the funds spent or can also be a breakthrough for people with DR who lack the funds for diagnosis in the hospital. Pre-processing process have a several steps such as green channel extraction, histogram equalization, filtering, optic disk removal with structuring elements on morphological operation, and contrast enhancement. Feature extraction of preprocessing result using GLCM and the data taken consists of contrast, correlation, energy, and homogeneity. The detected components in this study are blood vessels, microaneurysms, and hemorrhages. This study results what the accuracy of classification using SVM and feature from GLCM method is 82.35% for normal eye and DR, 100% for NPDR and PDR. So, this program can be used for diagnosing DR accurately.
AB - Diabetic Retinopathy (DR) is the cause of blindness. Early identification needed for prevent the DR. However, High hospital cost for eye examination makes many patients allow the DR to spread and lead to blindness. This study identifies DR patients by using color fundus image with SVM classification method. The purpose of this study is to minimize the funds spent or can also be a breakthrough for people with DR who lack the funds for diagnosis in the hospital. Pre-processing process have a several steps such as green channel extraction, histogram equalization, filtering, optic disk removal with structuring elements on morphological operation, and contrast enhancement. Feature extraction of preprocessing result using GLCM and the data taken consists of contrast, correlation, energy, and homogeneity. The detected components in this study are blood vessels, microaneurysms, and hemorrhages. This study results what the accuracy of classification using SVM and feature from GLCM method is 82.35% for normal eye and DR, 100% for NPDR and PDR. So, this program can be used for diagnosing DR accurately.
KW - Diabetic retinopathy
KW - SVM Classifier
UR - http://www.scopus.com/inward/record.url?scp=85069179932&partnerID=8YFLogxK
U2 - 10.1109/EECSI.2018.8752726
DO - 10.1109/EECSI.2018.8752726
M3 - Conference contribution
AN - SCOPUS:85069179932
T3 - International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)
SP - 154
EP - 160
BT - Proceedings - 2018 5th International Conference on Electrical Engineering Computer Science and Informatics, EECSI 2018
A2 - Stiawan, Deris
A2 - Subroto, Imam Much Ibnu
A2 - Riyadi, Munawar A.
A2 - Aditya, Christian Sri Kusuma
A2 - Has, Zulfatman
A2 - Yudhana, Anton
A2 - Minarno, Agus Eko
PB - Institute of Advanced Engineering and Science
T2 - 5th International Conference on Electrical Engineering Computer Science and Informatics, EECSI 2018
Y2 - 16 October 2018 through 18 October 2018
ER -
