Deep Learning and Machine Learning Algorithms With Ocular Disease Intelligent Recognition

Abstract

The pivotal role of initial fundus screening in ophthalmology lies in its efficiency and cost-effectiveness as a preventive measure against blindness resulting from eye diseases. However, the manual diagnosis process in clinical environments is time-consuming and, due to the scarcity of medical resources, can lead to a deterioration in patient conditions. Ocular diseases, which impair the normal functioning of the eye, have been the focus of considerable research, yielding promising results through the application of advanced deep learning (DL) and machine learning (ML) techniques. Acknowledging the urgent need for an effective classification model, this study advocates for the implementation and evaluation of sophisticated DL and ML algorithms to accurately identify ocular diseases from images of patients' left and right eyes. Utilizing a sample size 491 from the Ocular Disease Intelligent Recognition (ODIR) database, our study meticulously compares several Convolutional Neural Networks (CNNs) and ML techniques.