1. Machine learning of retinal pathology in optical coherence tomography images

    Machine learning of retinal pathology in optical coherence tomography images

    Background: Acute macular degeneration (AMD), central serous retinopathy (CSR), diabetic retinopathy (DR) and macular hole (MH) are common vision impairing pathologies in the field of ophthalmology. Machine learning with deep convolutional neural networks can be used to analyze ophthalmological diseases using fundus and optical coherence tomography (OCT) images, but with limited accuracy. In order to improve the sensitivity and specificity of these models, the objective of this study was to examine the effect of data augmentation on the performance of the neural network. Methods: OCT Images for above pathologies and normal eye were acquired from the Optical Coherence Tomography Image ...

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