期刊名称：TELKOMNIKA (Telecommunication Computing Electronics and Control)
印刷版ISSN：2302-9293
出版年度：2018
卷号：16
期号：1
页码：445-454
DOI：10.12928/telkomnika.v16i1.6188
语种：English
出版社：Universitas Ahmad Dahlan
其他摘要：Hypertensive retinopathy is characterized by changes in retinal vessels, a change known as tortuosity. Automated analysis of retinal vascular changes will make it easier for clinicians to make an initial diagnosis of the disease. The pattern of blood vessels in the retina of the eye can be approached with a fractal pattern. This study proposes a method for the early detection of disease hypertensive retinopathy using the fractal analysis approach fundus retinal image. Variable fractal used is the fractal di mension and lacunarity, whereas for the classification algorithm using ensemble Random Forest and validation using the k-fold cross-validation. Performance measurement using the parameters of accuracy, positive prediction value (PPV), negative prediction value (NPV), sensitivity, specificity and area under the curve (AUC). The test results using 10-fold cross-validation values obtained accuracy 88.0%, PPV 84.0%, NPV 92.0%, sensitivity 91.3%, specificity 85.19%, and 88.25% AUC. The performance is produced when using lacunarity the box size 22 . Based on the research results, it can be concluded that early detection of hypertensive retinopathy with fractal analysis approaches have a performance based on AUC produ ced included in good categories.
