TELKOMNIKA, Vol.17, No.1, February 2019, pp.463~472 ISSN: 1693-6930, accredited First Grade by Kemenristekdikti, Decree No: 21/E/KPT/2018 DOI: 10.12928/TELKOMNIKA.v17i1.11604  463 Received June 10, 2018; Revised November 9, 2018; Accepted December 7, 2018 Classification of neovascularization using convolutional neural network model Wahyudi Setiawan 1 , Moh. Imam Utoyo 2 , Riries Rulaningtyas *3 1 Informatics Department, University of Trunojoyo Madura, Bangkalan, Indonesia 1,2 Mathematics Department, University of Airlangga, Surabaya, Indonesia 3 Physics Department, University of Airlangga, Surabaya, Indonesia *Corresponding author, e-mail: riries-r@fst.unair.ac.id Abstract Neovascularization is a new vessel in the retina beside the artery-venous. Neovascularization can appear on the optic disk and the entire surface of the retina. The retina categorized in Proliferative Diabetic Retinopathy (PDR) if it has neovascularization. PDR is a severe Diabetic Retinopathy (DR). An image classification system between normal and neovascularization is here presented. The classification using Convolutional Neural Network (CNN) model and classification method such as Support Vector Machine, k-Nearest Neighbor, Naïve Bayes classifier, Discriminant Analysis, and Decision Tree. By far, there are no data patches of neovascularization for the process of classification. Data consist of normal, New Vessel on the Disc (NVD) and New Vessel Elsewhere (NVE). Images are taken from 2 databases, MESSIDOR and Retina Image Bank. The patches are made from a manual crop on the image that has been marked by experts as neovascularization. The dataset consists of 100 data patches. The test results using three scenarios obtained a classification accuracy of 90%-100% with linear loss cross validation 0%-26.67%. The test performs using a single Graphical Processing Unit (GPU). Keywords: classification, convolutional neural network, deep learning, diabetic retinopathy, neovascularization. Copyright © 2019 Universitas Ahmad Dahlan. All rights reserved. 1. Introduction Fundus image is a retinal image obtained from the fundus camera. An expert analyzes the fundus image to determine retina diseases. Diabetic Retinopathy (DR) is one of the retinal diseases. Usually, DR is present on Diabetes Mellitus (DM) patient for more than 15 years. DR causes blindness if not treated early [1]. A study in the UK, from 2004 to 2014, reported the number of people with DR. The study used a sample of 7,707,475 DM patients. The results showed a percentage of DR sufferers increases every year. In 2004, the number of patients with DR 0.9%, in 2014 increased to 2.3% of the DM patients [2]. Other studies, the people with DR in Southeast Asia, reported 35% of patients with DM [3]. Clinical examination of DR is done through several tests include biomicroscope, fluorescein angiography, fundus photo, and indocyanine green angiography. Also, experts perform Optical Coherence Tomography (OCT). The results of the analysis should be supported by age-related information, medical history, visual acuity, cardiovascular and disease progression [4, 5]. The examination procedures consist of several tests. It needs a relatively long time and expensive cost. An alternative solution is DR computationally detection. The degree of abnormality DR is Non-Proliferative Diabetic Retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR). This article has focused on PDR classification. The symptoms of PDR are new blood vessels on the surface of the retina or new blood vessels in the Optic Disk. Figure 1 shows PDR fundus images. There is widely research on PDR. Jelinek et al. have classified NPDR and PDR [6]. Goatman et al. have classified two classes, normal and abnormal [7]. Akram et al. have classified three classes, normal, NVD, and NVE [8]. Welikala et al. have classified two classes, PDR and NPDR [9]. Gupta et al. have classified three classes, NVE, NPDR and Normal [10]. The study performs image processing with conventional method phases. It includes preprocessing, segmentation, feature extraction, feature selection, and classification. The