A Classification Network for Ocular Diseases Based on Structure Feature and Visual Attention

With the rapid development of digital image processing and machine learning technology, computer-aided diagnosis for ocular diseases is more active in the medical image processing and analysis field. Optical coherence tomography (OCT), as one of the most promising new tomography techniques, has been widely used in the clinical diagnosis of ophthalmology and dentistry. To overcome the lack of professional ophthalmologists and realize the intelligent diagnosis of different ocular diseases, we propose a convolutional neural network (CNN) based on structure feature and visual attention for ocular diseases classification. We firstly preprocess the OCT images according to the OCT data characteristics to enhance the OCT image quality. Meanwhile, we propose to use the CNN with structure prior to classify five kinds of ocular diseases, including age-related macular degeneration (AMD), diabetic macular edema (DME), normal (NM), polypoidal choroidal vasculopathy (PCV), and pathologic myopia (PM). Besides, the visual attention mechanism is also used to enhance the ability of the network to represent effective features. The experimental results show that our method can outperform most of the state-of-the-art algorithms in the classification accuracy of different ocular diseases on the OCT dataset.