Abstract:

In this article, an automatic system is presented to detect age-related macular degeneration, which is the cause of 8.7% of visual impairment in the world. For the diagnosis of the disease convolutional neural network, which has recently been used in feature extraction from the image, has been utilized. In the study, the convolutional neural network was trained with 28031 labeled OCT images to classify age-related macular degeneration. The trained convolutional neural network was tested with 6900 OCT images. Xception, VGG16, Inception V3 and Resnet50 deep learning networks were used for training and testing. The trained networks were compared and the performance of the networks was evaluated for the OCT dataset. Classification of OCT images using Resnet50 network achieved 0.95, VGG16 network achieved 0.95, InceptionV3 network achieved 0.97 and Xception network achieved accuracy of 0.98. Xception network achieved F1 score of 0.96 which is highest rate of classification on the OCT dataset.

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