Diabetic Retinopathy Stages Classification using Improved Deep Learning
Petrisia Widyasari Sudarmadji, Prisca Deviani Pakan, Rocky Yefrenes Dillak
Abstract
Diabetic Retinopathy (DR) is the most common complication of diabetes mellitus which can cause a loss in vision. The stages of DR can be divided as no DR, non-proliferative DR, and proliferative DR. This paper proposed a method to classify stages of DR using deep learning and genetics algorithm. This research developed an optimal architecture using VGG basic architecture of a convolutional neural network. The results obtained from the Messidor database were 99.66 % accuracy, 99 % sensitivity, and 98 % specificity. Meanwhile, when tested with the Kaggle database the proposed method produced sensitivity, specificity, and accuracy of 98%, 97%, 98.43% respectively. These results show that the method could classify the DR images.