Improving Laser Mark Detection for Retinal Images based on the AlexNet Model

Abstract

Recently, the field of deep learning has received increased attention due to its high accuracy. A common deep learning technique is Convolutional Neural Networks (CNN), which is as a construction of trainable multi-stages using multiple phases. In this paper, we use a type of CNN called AlexNet to classify human retinal images into ‘normal’ or ‘have been treated using photocoagulation laser treatments’ classes. Indeed, this classification technique will help experts to examine any case and make the examination process faster and more efficient. The study was conducted through several experiments using 730 images of human retina that were either treated by laser or not treated. An average accuracy rate of more than 97% was obtained. Additionally, possible improvements and destiny traits are suggested to summarize this study. © 2020, World Academy of Research in Science and Engineering. All rights reserved.