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.
Citation:
Shyamalee, K.W.T. (2023). Computational model for glaucoma classification [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/22201