Comparative Analysis of Self-Supervised and Supervised Deep Learning Models for Ocular Disease Recognition

This study aims to compare the performance of supervised and self-supervised deep learning models in the field of ocular disease recognition. In this study evaluation of various metrics such as accuracy, loss, training loss, and validation loss is done to assess the effectiveness of these models. The results indicate that self-supervised learning shows competitive performance, highlighting its potential in the domain of ocular disease recognition. This finding suggests that self-supervised learning techniques can play a valuable role in improving the accuracy and effectiveness of ocular disease recognition systems