Retinal Disease for Clasification Multilabel with Applying Convolutional Neural Networks Based Support Vector Machine and DenseNet

Abstract

The retina is an essential part of the eye and works to transmit visual information to the brain. In maintaining the eye, an ophthalmologist needs regular examinations, but the price is expensive and takes time. Therefore, technological developments are expected to help the medical world to detect diseases. The technology is image processing. Convolutional Neural Network (CNN) is the most popular neural network model to handle image analysis and can recognize patterns from an image accurately. This study detected Drusens, Optic Disc Cupping, and Tessellation diseases using 534 fundus images. The architecture used Convolutional Neural Network-based Support Vector Machine (CNN based SVM) and DenseNet, which is a Convolutional Neural Network architecture development. In obtaining the best results, in this study, we use several variations of the optimizer, namely adam, nadam, and RMSprop, and the best results from this study can be seen from the accuracy value of 93,21% using the DenseNet architecture.