Computational model for glaucoma classification

Abstract

Glaucoma is a leading cause of blindness and affects millions of people worldwide. It is a chronic eye condition that damages the optic nerve and if left untreated, it can lead to vision loss and decreased quality of life. According to the World Health Organization, it affects approximately 65 million people worldwide. Thus, there is a requirement for an effective and reliable mechanism for the identification of Glaucoma. This study addresses a computational model for the Glaucoma identification process. The proposed system uses fundus images of the eye. The availability of computing resources and automated glaucoma diagnosis tools can now be supported due to recent developments in DL. Generic Convolutional Neural Networks (CNN) is still not frequently used in medical situations despite the advances made by deep learning in disease diagnosis using medical images. This is because of the limited trustworthiness of these models. Despite the rise in popularity of deep learning-based glaucoma classification in recent years, few studies have focused on the models’ explainability and interpretability, which boosts user confidence in such applications. To predict glaucoma conditions, this study uses state-of-the-art deep learning techniques to segment and classify fundus images. To make the results more understandable, visualization techniques are used to present the findings. Our forecasts are based on a modified InceptionV3 architecture and a U-Net with attention mechanisms. Additionally, us- ing Gradient-weighted Class Activation Mapping (Grad-CAM) and Grad-CAM++, we create heatmaps that show the areas that had an impact on the glaucoma diagnosis. With the RIM-ONE dataset, our findings demonstrate the best accuracy, sensitivity, and specificity values of 98.97%, 99.42%, and 95.59%, respectively. With the aid of fundus images, this model can be used to support automated glaucoma diagnosis.