Dian Candra Rini Novitasari, Fatmawati Fatmawati, Rimuljo Hendradi, Hetty Rohayani, Rinda Nariswari, Arnita Arnita, Moch Irfan Hadi, Rizal Amegia Saputra, Ardhin Primadewi
Diabetic retinopathy is the leading cause of blindness suffered by working-age adults. The increase in the population diagnosed with DR can be prevented by screening and early treatment of eye damage. This screening process can be conducted by utilizing deep learning techniques. In this study, the detection of DR severity was carried out using the hybrid CNN-DELM method (CDELM). The CNN architectures used were ResNet-18, ResNet-50, ResNet-101, GoogleNet, and DenseNet. The learning outcome features were further classified using the DELM algorithm. The comparison of CNN architecture aimed to find the best CNN architecture for fundus image features extraction. This research also compared the effect of using the kernel function on the performance of DELM in fundus image classification. All experiments using CDELM showed maximum results, with an accuracy of 100% in the DRIVE data and the two-class MESSIDOR data. Meanwhile, the best results obtained in the MESSIDOR 4 class data reached 98.20%. The advantage of the DELM method compared to the conventional CNN method is that the training time duration is much shorter. CNN takes an average of 30 min for training, while the CDELM method takes only an average of 2.5 min. Based on the value of accuracy and duration of training time, the CDELM method had better performance than the conventional CNN method. © 2022 by the authors.
Mathematics Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Surabaya, 36743, Indonesia; Mathematics Department, Faculty of Science and Technology, Universitas Airlangga, Surabaya, 36743, Indonesia; Sains and Technology Faculty, Muhammadiyah Jambi University, Jambi, 23265, Indonesia; Statistics Department, School of Computer Science, Bina Nusantara University, Jakarta, 10110, Indonesia; Department of Mathematics, Universitas Negeri Medan, Medan, 20028, Indonesia; Biology Department, Faculty of Science and Technology, UIN Sunan Ampel Surabaya, Surabaya, 36743, Indonesia; Information System Department, Faculty of Engineering and Informatics, Universitas Bina Sarana Informatika, Sukabumi, 43111, Indonesia; Department of Informatics Engineering, Muhammadiyah University of Magelang, Magelang, 56111, Indonesia
