Optical coherence tomography (OCT) is being increasingly adopted as a label-free and non-invasive technique for biomedical applications such as cancer and ocular disease diagnosis. Diagnostic information for these tissues is manifest in textural and geometric features of the OCT images, which are used by human expertise to interpret and triage. However, it suffers delays due to the long process of the conventional diagnostic procedure and shortage of human expertise. Here, a custom deep learning architecture, LightOCT, is proposed for the classification of OCT images into diagnostically relevant classes. LightOCT is a convolutional neural network with only two convolutional layers and a fully connected layer, but it is shown to provide excellent training and test results for diverse OCT image datasets. We show that LightOCT provides 98.9% accuracy in classifying 44 normal and 44 malignant (invasive ductal carcinoma) breast tissue volumetric OCT images. Also, >96% accuracy in classifying public datasets of ocular OCT images as normal, age-related macular degeneration and diabetic macular edema. Additionally, we show ∼96% test accuracy for classifying retinal images as belonging to choroidal neovascularization, diabetic macular edema, drusen, and normal samples on a large public dataset of more than 100,000 images. The performance of the architecture is compared with transfer learning based deep neural networks. Through this, we show that LightOCT can provide significant diagnostic support for a variety of OCT images with sufficient training and minimal hyper-parameter tuning. The trained LightOCT networks for the three-classification problem will be released online to support transfer learning on other datasets.

中文翻译：

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深度学习架构“ LightOCT”，用于使用生物样本的光学相干层析成像图像进行诊断决策支持

光学相干断层扫描（OCT）被越来越多地用作无标签和非侵入性技术，用于生物医学应用，例如癌症和眼部疾病的诊断。这些组织的诊断信息体现在OCT图像的纹理和几何特征中，人类专业知识将其用于解释和分类。然而，由于常规诊断程序的过程长且缺乏专业知识，因此延迟。在这里，提出了一种定制的深度学习架构LightOCT，用于将OCT图像分类为诊断相关的类别。LightOCT是一个只有两个卷积层和一个完全连接层的卷积神经网络，但它显示出可为各种OCT图像数据集提供出色的训练和测试结果。我们证明LightOCT提供了98。在对44例正常和44例恶性（浸润性导管癌）乳腺组织体积OCT图像进行分类时，准确率达到9％。同样，将眼部OCT图像的公共数据集分类为正常，与年龄相关的黄斑变性和糖尿病性黄斑水肿的准确率> 96％。此外，在超过100,000张图像的大型公共数据集上，我们将视网膜图像分类为脉络膜新生血管，糖尿病性黄斑水肿，玻璃膜疣和正常样本的测试准确率约为96％。将该架构的性能与基于转移学习的深度神经网络进行了比较。通过这种方式，我们表明LightOCT可以通过足够的训练和最少的超参数调整为各种OCT图像提供重要的诊断支持。