Evaluating Deep Learning Techniques for Early Detection of Glaucoma

The goal of this study is to apply sophisticated computational methods to identify glaucoma, a major cause of vision impairment. The system analyzes ocular pictures to identify patterns suggestive of glaucoma using convolutional neural networks (CNN) and deep learning architectures like ResNet, VGG, EfficientNet, MobileNet, and DenseNet. The method improves classification accuracy by using optimized neural network topologies and organized datasets. A variety of preprocessing methods guarantee that the input data is refined, which enhances model performance. The framework is intended to support clinical decision-making by aiding in early detection. This study aims to increase the accuracy and efficiency of diagnostics by showcasing the possibilities of automated analysis in ophthalmology.