Authors - Suman Kumar Mandal, Wendrila Biswas, Jaydev Mishra Abstract - Glaucoma is an optic neuropathy that is progressive and one of the most common causes of permanent blindness in the world. The retinal fundus images used to diagnose the condition are still time-consuming and highly reliant on the clinical expertise to detect the condition early, before the loss of vision becomes severe. In this experiment, we suggest a deep learning model that will use the ResNet50 architecture to identify retinal fundus images as belonging to one of two categories: Referable Glaucoma (RG) and Non-Referable Glaucoma (NRG). ResNet50 has been selected because it has good feature ex-traction (residual learning and deep convolutional learning). The standard performance measures were used to assess the trained model, such as accuracy, precision, recall, F1-score, and area under the ROC curve. The experimental findings indicate that the suggested approach yields consistent and accurate classification of RG and NRG cases, and it can be used to assist the ophthalmologist in clinical decision-making. The paper demonstrates how deep learning models could assist in further development of early glaucoma detection and mass screening, which, in their turn, can contribute to better patient outcomes and prevention of blindness before its onset.
Open Zoom