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Firmansjah, Muhammad
Airlangga University
Computer Science & IT Electrical & Electronics Engineering

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Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier Foeady, Ahmad Zoebad; Rini Novitasari, Dian Candra; Asyhar, Ahmad Hanif; Firmansjah, Muhammad
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.455 KB) | DOI: 10.11591/eecsi.v5.1630

Abstract

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.
Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier Ahmad Zoebad Foeady; Dian Candra Rini Novitasari; Ahmad Hanif Asyhar; Muhammad Firmansjah
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (605.455 KB) | DOI: 10.11591/eecsi.v5.1630

Abstract

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.
Co-Authors Ahmad Hanif Asyhar Ahmad Zoebad Foeady Dian Candra Rini Novitasari Dian Candra Rini Novitasari Foeady, Ahmad Zoebad