  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.
