Enhancing Glaucoma Diagnosis Through Image Processing and Supervised Learning

Glaucoma, a condition causing permanent blindness, is often symptomless, making early diagnosis challenging. The pipeline employing Convolutional Neural Networks (CNNs), provides an interpretable Glaucoma assessment, including confidence levels and structural segmentations. Integrated into a mobile app, the pipeline's efficiency was evaluated in terms of time and space complexities, showcasing its potential for widespread Glaucoma screening. The deep learning architecture, featuring six layers with dropout and data augmentation, demonstrated robust diagnostic performance n ORIGA and ACRIMA datasets.