论文标题
使用完全卷积神经网络从原始绕过毛细血管oct图像中检测的青光眼检测
Glaucoma Detection From Raw Circumapillary OCT Images Using Fully Convolutional Neural Networks
论文作者
论文摘要
如今，青光眼是全球失明的主要原因。我们在本文中提出了两种基于深度学习的方法，以解决原始电球帕皮里OCT图像中的青光眼检测。第一个是基于从头开始训练的卷积神经网络（CNN）的发展。第二个是微调一些最常见的最新CNNS架构。实验是在围绕视网膜的视神经头周围的93个青光眼和156个正常的B扫描组成的私人数据库进行的，该数据库由专家眼科医生诊断。验证结果证据表明，在解决小数据库时，微型CNN的表现优于从头开始训练的网络。此外，网络家族报告了最有希望的结果，在独立测试集的预测期间，ROC曲线下的面积为0.96，精度为0.92。
Nowadays, glaucoma is the leading cause of blindness worldwide. We propose in this paper two different deep-learning-based approaches to address glaucoma detection just from raw circumpapillary OCT images. The first one is based on the development of convolutional neural networks (CNNs) trained from scratch. The second one lies in fine-tuning some of the most common state-of-the-art CNNs architectures. The experiments were performed on a private database composed of 93 glaucomatous and 156 normal B-scans around the optic nerve head of the retina, which were diagnosed by expert ophthalmologists. The validation results evidence that fine-tuned CNNs outperform the networks trained from scratch when small databases are addressed. Additionally, the VGG family of networks reports the most promising results, with an area under the ROC curve of 0.96 and an accuracy of 0.92, during the prediction of the independent test set.
