Klasifikasi Tingkat Keparahan Penyakit Diabetic Retinopathy menggunakan Convolutional Neural Network
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Ni Luh Wiwik Sri Rahayu Ginantra
*
Institut Bisnis dan Teknologi Indonesia, Denpasar,
Indonesia
- Theresia Hendrawati Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia
- I Kadek Diksa Bayu Prasetya Institut Bisnis dan Teknologi Indonesia, Denpasar, Indonesia (*) Corresponding Author
Abstract
Diabetic Retinopathy is an eye condition in Diabetes sufferers that causes damage to the retina, which can result in permanent blindness if not treated properly. The initial stage of this disease is the widening of the blood vessels in the eye which, if left untreated, can cause the formation of new blood vessels which can cover the retina of the eye, thereby increasing the risk of vision loss. There are several classes of Diabetic Retinopathy disease; to determine the class you can use the Deep Learning method which can model various data such as images. The classification process is carried out by training a Convolutional Neural Network model on a disease image dataset taken from the Kaggel repository with a total of 5 classes. This research uses a Fine Tuning strategy and the EfficientNetB1 model to determine the performance of the CNN model in the Diabetic Retinopathy Classification process. Based on training results, the EfficientNetB1 model produces 92.51% accuracy in detecting Diabetic Retinopathy. These results show that the model can provide optimal results in the dataset training process.
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