Harnessing Predictive Modelling for Early Diagnosis of Glaucoma

Glaucoma is a condition that often causes irreparable blindness; successful treatment requires early diagnosis. In order to diagnose glaucoma, the recommended approach takes features and divides them using the UNet design. This combined strategy that combines the feature extraction and segmentation methods streamlines the diagnosing procedure. Making use of UNet's spatial interaction capabilities, the framework accurately differentiates between the optic disc and cup sections, a crucial step in the diagnosis of glaucoma. UNet also improves the accuracy of diagnosis by simplifying the process of extracting trustworthy information from segmented regions. Good validation results are obtained using a heterogeneous dataset that includes images from multiple sources. The framework has remarkable sensitivity, specificity and accuracy, suggesting its potential for pragmatic clinical applications.