Diagnosis of glaucoma from retinal fundus images using disc localization and sequential DNN model

Abstract

AbstractDeep learning is an emerging trend with enormous applications over the past few years. Ophthalmology is one such area in medical applications where early disease detection is required to avoid loss of vision. Glaucoma is a rapidly growing disorder related to human eye, which arises due to the increase in pressure inside the eye. The medical diagnosis methods available for glaucoma have some limitations; hence, computer‐aided design (CAD) approach is preferred using images. In the context of image processing, convolution neural networks (CNNs) are preferred for classification because of their ability to grasp highly discriminate features from raw pixel intensities. In our approach, diagnosis of glaucoma is implemented by extracting the region of interest (ROI) by splitting the coefficients into recurrence decays and will improve the possibility of identifying even poorly differentiated exudates and upgrading the normal recurrence ranges. Later, a sequential deep neural network (DNN) model with a rectified linear unit (ReLU) and sigmoid function is designed to train the data with effective features matching from training and testing samples. The proposed model is implemented on two publicly available datasets (Drishti‐GS1 and ACRIMA) using 10‐fold cross validation (CV), 60:40 and 70:30 split ratio approaches, and performance is assessed using the metrics and plotted the region of convergence curves. The model is also tested on two more datasets (ORIGA and Refuge) to validate the robustness of the proposed model. The obtained simulation results and the evaluated performance metrics prove that our proposed model diagnose glaucoma from retinal fundus images effectively compared with other existing models.