Automated Glaucoma Detection and Classification via Deep Neural Networks and Feature Extraction

Glaucoma, a progressive visual neuropathy, is a leading cause of lifelong blindness worldwide. Early identification and accurate classification are crucial for rapid treatment of glaucoma and preservation of vision. The research suggests a new approach to identify and detect glaucoma by integrating deep neural networks with advanced feature extraction methods. The proposed method involves selecting relevant features from retinal images to detect subtle patterns indicating signs of glaucomatous changes. A deep neural network architecture is then utilized to process these characteristics for accurate and automated classification. The effectiveness of the proposed strategy in obtaining high accuracy, sensitivity, and specificity in detecting and classifying glaucoma is supported by experimental results on a large dataset. Deep neural networks and feature extraction synergize effectively as a promising approach to enhance automated glaucoma detection systems.