GLAUCOMA DETECTION USING CONVOLUTION NEURAL NETWORKS

ELECTRONICS AND COMMUNICATION ENGINEERING.

GLAUCOMA is an unending and irreversible eye infection in which the optic nerve is consistently hurt, causing disintegration in vision and individual fulfillment. Glaucoma is rated as the leading cause of blindness in the working age population all over the world. Glaucoma is also rated as the leading cause of irreversible vision loss worldwide. Early detection of glaucoma is important for providing timely treatment and minimizing vision loss. The diagnosis of Glaucoma through color fundus images requires experienced clinicians to identify the presence and significance of many small features which, along with the complex grading system, makes this difficult and timeconsuming task. Hence this issue is the right problem that can be solved by automatically diagnosing glaucoma with the help of the deep learning approaches. In this project Glaucoma is detected using a network with Convolution Neural Network (CNN) architecture. Convolutional Neural Networks (CNN’s) are appropriate to find the solution for this type of issue as they can extract various levels of data from the input image, and which encourages to differentiate among non-glaucomic and glaucomic images. This model is trained and tested on a kaggle dataset.

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