Optical Coherence Tomography Classification through Deep Learning

Abstract

Non-invasive tomography can be performed using optical coherence tomography (OCT), a new imaging technology. Nowadays, one of the most advanced computer vision algorithms is deep learning. Due to the increasing use of OCT pictures in the medical treatment of Age-related Macular Degeneration (AMD), an increasing volume of data must be analyzed.It may be possible to handle enormous amounts of medical picture data using machine learning algorithms. Convolutional neural networks (CNNs) are among the best approaches for image processing in Deep Learning. A segmentation technique was used by these researchers to construct deep-learning algorithms that could distinguish AMD from healthy OCT scans and AMD even without exudative changes.