1. Integrating Handcrafted and Deep Features for Optical Coherence Tomography Based Retinal Disease Classification

    Integrating Handcrafted and Deep Features for Optical Coherence Tomography Based Retinal Disease Classification

    Deep Neural Networks (DNNs) have been widely applied to automatic analysis of medical images for disease diagnosis, and to help human experts by efficiently processing immense amounts of images. While handcrafted feature has been used for eye disease detection or classification since the 1990s, DNN was recently adopted in this area and showed very promising performance. Since handcrafted and deep feature can extract complementary information, we propose in this paper three different integration frameworks to combine handcrafted and deep feature for optical coherence tomography (OCT) image based eye disease classification. In addition, to integrate the handcrafted feature at Input and ...

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