Glaucoma Detection Using Deep Learning Techniques

One of the main causes of irreversible blindness is glaucoma, which frequently advances symptomless until it reaches an advanced stage. Fundus and optical coherence tomography (OCT) pictures must be expertly assessed for traditional diagnosis, which is laborious and prone to mistakes. Convolutional Neural Networks (CNNs) are used in this paper’s deep learning-based diagnostic framework to automatically detect glaucoma. The suggested model extracts characteristics from both fundus and OCT data after being trained on a sizable dataset of labelled ocular images. When compared to conventional techniques, experimental results demonstrate increased accuracy, sensitivity, and specificity. The model shows promise in helping ophthalmologists detect glaucoma in a timely and accurate manner.