Eye disease detection using yolo and ensembled googlenet

Abstract

Our research work has succeeded in integrating ensemble into GoogleNet image classification technique aiming higher accuracy and performance than existing models. The convolutional layers are apt for feature extraction from images. In GoogleNet classifier, only 1 fully connected dense layer (weak) is used for classification of class from these outputted features. But we integrated ensemble immediately after convolutional layers for purpose of better classification output. Thus, the output (features of image) of convolutional layers is passed as a separate input to both ensemble methods and fully connected layers of GoogleNet for obtaining the class of image. The final class of image is determined by the specific strategy after analyzing outputs of ensemble and GoogleNet fully connected layer. All earlier works focused on eye disease classification. Here, we have also experimented with YOLO for detection of location and class of diseases. The eye is considered as the most significant part of the body. But this most significant part of the body is easily subjected to various kinds of diseases. Early detection and prevention are needed. Our research work aimed at detecting top five common eye diseases with higher accuracy. User can upload a pic in a mobile or cloud application, and inbuilt AI algorithms will detect the type of eye disease with higher accuracy and thus offering prevention suggestions at an early stage without doctor intervention.