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We present an automatic classification algorithm for retinal optical coherence tomography (OCT) images based on convolution neural network (CNN). This algorithm inherently contains feature extraction and classification, thus avoiding the design feature extractor manually. Firstly, we processed the OCT images to focus on and determine the pathological area of the retinal OCT images, and to speed up the training of the network. Then we input the original images to crop them, which can effectively prevent the noise introduced in the processes of image processing and changing the pixels in the original image. Secondly, we augmented the OCT images in the source data set to obtain sufficient images, and to alleviate the impact of a relatively small number of target classification images on the model accuracy and generalization ability. Our method was introduced the random translation in image cropping and horizontal flipped to augment the OCT images. Then we applied two methods to build two data sets used to train the network, and we divided each of the data sets into a training set and a validation set. Next, we designed an efficient classification network and trained it with the two training sets respectively, to acquire the two models that can classify OCT images. The results indicate that the network trained by the augmented data can classify images more effectively. In our classification algorithm, the accuracy, the sensitivity and the specificity are 93.43%, 91.38%, and 95.88%, respectively.
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