Penerapan Algoritma Convolutional Neural Network Untuk Menentukan Retinopati Hipertensi Melalui Citra Retina Fundus
DOI:
https://doi.org/10.34012/jutikomp.v6i2.4307Keywords:
Convolutional Neural Network, Eye Fundus Images, Hypertensive RetinopathyAbstract
Hypertension is a disease that spreads in the human body caused by increased blood pressure that exceeds normal limits. The increase occurs over a long period, causing complications in human organs that cannot be seen clearly, such as complications in the heart, kidneys, brain, and retina. One of the disorders or complications of high blood pressure is in the retina. The disorder in the retina can also be said as hypertensive retinopathy. Patients suffering from hypertensive retinopathy can only be diagnosed by an ophthalmologist; this is because hypertensive retinopathy cannot be seen with the naked eye. However, one of the earliest signs is the thinning of the arterioles, which can cause blindness. Therefore, computer-assisted processing and analysis of eye fundus images to identify hypertensive retinopathy is an important thing to do by applying the Convolutional Neural Network algorithm. There are nine Convolutional Neural Network architectures used, namely AlexNet, DenseNet, Inception-V3, InceptionResNetV2, Lenet-5, MobileNetV2, ResNet50, VGG16, and VGG19. Based on the experimental results, it was found that of the nine Convolutional Neural Network architectures, two of them, namely AlexNet and Lenet-5, obtained an F1 Measure value of 0.66 and the highest accuracy of 0.67.
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