A novel two-branched deep convolutional (TWEEC) network is developed for computer-aided glaucoma diagnosis. The TWEEC network is designed to simultaneously extract anatomical information related to the optic disc and surrounding blood vessels which are the retinal structures most affected by glaucoma progression. The spatial retinal images and wavelet approximation subbands are compared as inputs to the proposed network. TWEEC's performance is compared to three implemented convolutional networks, one of which employs transfer learning. Experiments showed that the introduced TWEEC network achieved accuracies of 98.78% and 96.34% for the spatial and wavelet inputs, respectively, by that outperforming the other three deep networks by 8-15%. This work paves the way for the development of efficient deep learning based computer-aided glaucoma diagnosis tools. Moreover, the present study illustrates that considering specific wavelet subbands for the training of convolutional networks can result in reliable performance with the advantage of reduced overall network training time.

中文翻译：

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TWEEC：使用深度学习技术从视网膜图像进行计算机辅助青光眼诊断

一种新型的双分支深度卷积 (TWEEC) 网络被开发用于计算机辅助青光眼诊断。TWEEC 网络旨在同时提取与视盘和周围血管相关的解剖信息，这些血管是受青光眼进展影响最大的视网膜结构。将空间视网膜图像和小波近似子带作为所提出网络的输入进行比较。TWEEC 的性能与三个实施的卷积网络进行了比较，其中一个采用了迁移学习。实验表明，引入的 TWEEC 网络在空间和小波输入方面的准确率分别达到了 98.78% 和 96.34%，比其他三个深度网络高 8-15%。