SCRD-Net: A Deep Convolutional Neural Network Model for Glaucoma Detection in Retina Tomography

Abstract

Early and accurate diagnosis of glaucoma is critical for avoiding human vision deterioration and preventing blindness. A deep-neural-network model has been developed for the diagnosis of glaucoma based on Heidelberg retina tomography (HRT), called "Seeking Common Features and Reserving Differences Net"(SCRD-Net) to make full use of the HRT data. In this work, the proposed SCRD-Net model achieved an area under the curve (AUC) of 94.0%. For the two HRT image modalities, the model sensitivities were 91.2% and 78.3% at specificities of 0.85 and 0.95, respectively. These results demonstrate a significant improvement over earlier results. In addition, we visualized the network outputs to develop an interpretation of the learned mechanism for discriminating glaucoma and normal images. Thus, the SCRD-Net can be an effective diagnostic indicator of glaucoma during clinical screening. To facilitate SCRD-Net utilization by the scientific community, the code implementation will be made publicly available.