Optical Coherence Tomography is a technique used to scan the Retina of the eye and check for tears. In this paper, we develop a Convolutional Neural Network Architecture for OCT scan classification. The model is trained to detect Retinal tears from an OCT scan and classify the type of tear. We designed a block-based approach to accompany a pre-trained VGG-19 using Transfer Learning by writing customised layers in blocks for better feature extraction. The approach achieved substantially better results than the baseline we initially started out with.

翻译：光学一致性地形学是一种用于扫描眼睛视网膜和检查眼泪的技术。 在本文中, 我们开发了一个用于 OCT 扫描分类的进化神经网络架构。 该模型经过培训, 以检测 OCT 扫描的视网膜眼泪并对撕裂类型进行分类。 我们设计了一个基于街区的方法, 与经过预先训练的VGG-19 转移学习相配套, 将自定义的层分写成块, 以更好地提取特征 。 这种方法取得了比我们最初开始的基线要好得多的结果 。