Data and image processing for intelligent glaucoma detection and optic disc segmentation using deep convolutional neural network architecture

Glaucoma, a major reason for incurable blindness globally, is still a significant public health issue. Existing diagnostic techniques are extremely clinician-reliant and time-consuming, resulting in undue delays in detection and treatment. This work proposes a new intelligent approach using Deep Convolutional Neural Networks (DCNNs) for the detection of glaucoma and optic disc segmentation from ophthalmic medical imaging. The suggested methodology includes preprocessing retinal fundus images to improve quality, followed by feature extraction through a DCNN structure optimized for glaucoma detection. Segmentation of the optic disc is performed through the VGG-19 model. Performance metrics confirm the efficiency of the suggested approach. The constructed DCNN model proves 98.69% accuracy in differentiating between glaucomatous and non-glaucomatous eyes, far exceeding conventional methods (85–90%). The model has a 95.18% recall, which means that the majority of actual glaucoma cases are identified correctly, and an F1-score of 96.84%, which reflects a good balance between precision and recall. In addition, with an AUC-ROC of 97.63%, the model is able to distinguish glaucomatous eyes well. Experimental results validate that the proposed approach improves accuracy and efficiency compared to current methods. This work contributes to the development of automated glaucoma detection algorithms with potential clinical uses in ophthalmology and medical imaging.