Abstract
The diagnosis of hypertensive retinopathy (CAD-RH) can be made by observing the tortuosity of the retinal vessels. Tortuosity is a feature that is able to show the characteristics of normal or abnormal blood vessels. This study aims to analyze the performance of the CAD-RH system based on feature extraction tortuosity of retinal blood vessels. This study uses a segmentation method based on clustering self-organizing maps (SOM) combined with feature extraction, feature selection, and the ensemble Adaptive Boosting (AdaBoost) classification algorithm. Feature extraction was performed using fractal analysis with the box-counting method, lacunarity with the gliding box method, and invariant moment. Feature selection is done by using the information gain method, to rank all the features that are produced, furthermore, it is selected by referring to the gain value. The best system performance is generated in the number of clusters 2 with fractal dimension, lacunarity with box size 22-29, and invariant moment M1 and M3. Performance in these conditions is able to provide 84% sensitivity, 88% specificity, 7.0 likelihood ratio positive (LR+), and 86% area under the curve (AUC). This model is also better than a number of ensemble algorithms, such as bagging and random forest. Referring to these results, it can be concluded that the use of this model can be an alternative to CAD-RH, where the resulting performance is in a good category.
Highlights
Hypertension can be detected when we are diligent in checking blood pressure
The core stages in this research are divided into 6 stages, namely preprocessing, segmentation, feature extraction, feature selection, classification, and performance analysis
The results showed that there was an AdaBoost better than random forest and support vector machine (SVM)
Summary
Hypertension can be detected when we are diligent in checking blood pressure. Hypertension can cause severe health complications and increase the risk of heart disease, stroke, and sometimes death. There is the thickening of the blood vessels, which in turn can disrupt blood flow to the retina. The hypertensive retinopathy diagnosis model generally has preprocessing, segmentation, feature extraction, classification, and performance analysis stages[1]. The segmentation process was carried out on 50 retinal images. Image segmentation is performed using the SOM clustering algorithm. The step is feature selection, using the information gain algorithm
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