Exploring Efficientnet and Vgg-16 for Early Detection of Ocular Diseases: A Model Comparison

Abstract

Well-timed and accurate prediction of ocular disorders is critical in preventing vision impairment and blindness. Convolutional Neural Networks (CNNs) have validated significant potential in scientific imaging programs, such as the identity of eye-associated disorders. This study aims to have a look at exploring a comparison among outstanding CNN architectures—EfficientNet and VGG-sixteen for predicting ocular diseases with the use of eye images. The evaluation specializes in comparing those fashions primarily based on vital metrics consisting of precision, recall, accuracy, and computational efficiency. EfficientNet, recognized for its scalable architecture and high performing design, is compared against VGG-16, a traditional deep learning model with a huge success in picture classification tasks. The models are trained and tested using a publicly available dataset of ocular images, and the finding reveal both the advantages and drawbacks of each architecture in terms of prediction accuracy, complexity, and inference time. This comparative study gives treasured steering for practitioners and researchers in choosing an optimal deep learning model for ophthalmic disease prediction and highlights the importance of balancing model performance with computational cost in real-world medical applications.