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

机译：SCRD-NET：视网膜断层扫描中青光眼检测的深度卷积神经网络模型

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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.
机译：早期和准确的青光眼诊断对于避免人类视力恶化和预防失明至关重要。已经开发了一种深神经网络模型，用于诊断基于海德堡视网膜断层扫描（HRT）的青光眼，称为“寻求共同特征和保留差异”（SCRD-Net）来充分利用HRT数据。在这项工作中，所提出的SCRD净模型在曲线（AUC）下实现了94.0％的区域。对于两个HRT图像方式，分别在0.85和0.95的特异性分别为91.2％和78.3％。这些结果表现出对早期结果的显着改善。此外，我们可视化网络输出，以制定对鉴别青光眼和正常图像的学习机制的解释。因此，SCRD-NET可以是临床筛查期间青光眼的有效诊断指标。为促进科学界的斯克特净利用，将公开可用的代码实施。

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