Ophthalmologist-Level Classification of Fundus Disease With Deep Neural Networks

PURPOSE: To implement the classification of fundus diseases using deep convolutional neural networks (CNN), which is trained end-to-end from fundus images directly, the only input are pixels and disease labels, and the output is a probability distribution of a fundus image belonging to 18 fundus diseases. METHODS: Automated classification of fundus diseases using images is a challenging task owing to the fine-grained variability in the appearance of fundus lesions. Deep CNNs show potential for general and highly variable tasks across many fine-grained object categories. Deep CNNs need large amounts of labeled samples, yet the available fundus images, especially labeled samples, are limited, which cannot satisfy the training requirement. So image augmentations such as rotation, scaling, and noising are implemented to enlarge the training dataset. We fine-tune the ResNet CNN architecture with 120,100 fundus images consisting of 18 different diseases and use it to classify the fundus images into corresponding diseases. RESULTS: The performance is tested against two board-certified ophthalmologists. The CNN achieves performance on par with the experts for the classification accuracy. CONCLUSIONS: Deep CNN is capable of predicting fundus diseases given fundus images as input, which can enhance the efficiency of diagnosis process and promote better visual outcomes. Outfitted with deep neural networks, mobile devices can potentially extend the reach of ophthalmologists outside of the clinic and provide low-cost universal access to vital diagnostic care. TRANSLATIONAL RELEVANCE: This article implemented automatic prediction of fundus diseases that was done by ophthalmologists previously.