Implementation of convolutional neural network for predicting glaucoma from fundus images

The contributions of this paper are two-fold. First, it uses machine learning tools to detect and monitor glaucoma. Second, it provides insight into medicine, which can assist healthcare professionals in improving disease diagnostic accuracy and help to reduce the progression and degeneration of retinal ganglion cells in patients. Glaucoma is a group of eye diseases that damage the optic nerve, which can cause vision loss and blindness at any age. The main symptom of glaucoma in the early stages is high internal eye pressure. It is still unknown what causes these diseases to develop, but if not treated, they result in optic nerve atrophy. For this reason, in this paper we propose a novel deep learning system for the automatic diagnosis of glaucoma using a convolutional neural network for classification, which demonstrates improved performance and records computation time for fundus images. The results showed an accuracy of 94 % and a loss value of only 0.27. The model we have created to investigate with Keras helped us achieve good results in our training and testing process. These study results demonstrate the ability of a deep learning model to identify glaucoma from fundus images. Increasing the filter size and training the model resulted in a higher accuracy rate. A population survey that was conducted in 2019 shows that most patients with glaucoma become aware of their disease late, after the disease causes a high level of optic nerve damage and a high percentage of vision loss. Early diagnosis and detection of glaucoma using optic nerve imaging technology have gained wide clinical interest in stopping or slowing the progression of the disease, allowing the development of new algorithms to automate the diagnosis of eye diseases

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