Effective Classification of Ocular Disease Using Resnet-50 in Comparison with Squeezenet

In order to increase the accuracy of identifying ocular illnesses, the suggested work aims to simulate and compare innovative resnet 50 with squeezenet. Using the Squeezenet algorithm and Matlab software, a unique application was designed with 10 samples compared to the unique Resnet-50, with a significance value of 0.03. G power is 0.8, beta value is 0.2, and alpha is 0.05. The Squeezenet approach with a novel remote application has a better statistical significance value (p > 0.05), according to this study. When predicting real-time ocular disorders, the Novel Resnet-50 method yields a result of 85.7 as opposed to Squeezenet's 81.2.