Classification of pathological images in ophthalmic optical coherence tomography is very important for the examination and analysis of macular diseases. However, manual classification is a time taking process and diagnosis can vary on disease to disease. Proposed work presents an automatic process for classification of ophthalmic images in Optical Coherence Tomography scanned images using Convolution Neural Network. We validate our results with this method on 243 ophthalmic images. This method accurately classifies retinal images in diseased eyes more closely to an expert grader as compared to a second expert grader.
Volume 12 | 03-Special Issue
Pages: 936-942
DOI: 10.5373/JARDCS/V12SP3/20201337