Detection of Glaucoma using Convloutional Neural Network

Abstract

Glaucoma, a very complex heterogeneous disease, is the leading cause for optic nerve-related blindness worldwide. Glaucoma is a chronic and irreversible eye disease, which leads to deterioration in vision and quality of life. it is estimated that approximately 60 million people will be affected by the year 2020. For this reason, we developed a system that automatically detects glaucoma. The objective of this research work is to carry out experiments with Convolutional Neural Networks to achieve the automatic detection of this disease. The experiments performed and obtained an average accuracy of 93%. This paper describes, the development of deep learning (DL) architecture with a convolutional neural network for automated glaucoma diagnosis. Deep learning systems, such as convolutional neural networks, can infer a hierarchical representation of images to discriminate between glaucoma and non-glaucoma patterns for diagnostic decisions. The proposed DL architecture contains Ten learned layers: Six convolutional layers and Four fully-connected layers. Dropout and Data Augmentation strategies are adopted to further boost the performance of glaucoma diagnosis. Extensive experiments are performed on the Online database of Kims Hospital.