A Method for the Automatic Detection of Myopia in Optos Fundus Images Based on Deep Learning.

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Abstract

Myopia detection is significant for preventing irreversible visual impairment and diagnosing myopic retinopathy. To improve the detection efficiency and accuracy, a Myopia Detection Network (MDNet) that combines the advantages of dense connection and Residual Squeeze-and-Excitation attention is proposed in this paper to automatically detect myopia in Optos fundus images. First, an automatic optic disc recognition method is applied to extract the Regions of Interest (ROIs) and remove the noise disturbances; then, data augmentation techniques are implemented to enlarge the data set and prevent overfitting; moreover, an MDNet composed of Attention Dense (AD) blocks is constructed to detect myopia in Optos fundus images. The results show that the Mean Absolute Error (MAE) of the Spherical Equivalent detected by this network can reach 1.1150 D (diopter), which verifies the feasibility and applicability of this method for the automatic detection of myopia in Optos fundus images.
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