A Novel Strategy to Segment the Retinal Layers using Convolutional Neural Network

It is essential to divide various layers of the retina to correctly diagnose and treat retinal diseases. Using optical coherence tomography (OCT) images and convolutional neural networks (CNNs), this study describes a novel deep learning approach for automatically separating retinal layers. The suggested method employs a U-Net architecture that has been trained on OCT dataset containing images of retinal segmentation and disease diagnosis, as well as their corresponding comments. The provided data was used to train this design with U-Net training data. When a data-based method is evaluated using a public dataset, it is possible to obtain results that are considered cutting-edge in terms of both accuracy and processing speed. The results suggest that using CNN to distinguish the layers of the retina could enhance both the diagnosis and treatment of eye diseases. This has the potential to be a significant advancement in the field.