A lightweight deep learning model for automatic segmentation and analysis of ophthalmic images.

ABSTRACT: Detection, diagnosis, and treatment of ophthalmic diseases depend on extraction of information (features and/or their dimensions) from the images. Deep learning (DL) model are crucial for the automation of it. Here, we report on the development of a lightweight DL model, which can precisely segment/detect the required features automatically. The model utilizes dimensionality reduction of image to extract important features, and channel contraction to allow only the required high-level features necessary for reconstruction of segmented feature image. Performance of present model in detection of glaucoma from optical coherence tomography angiography (OCTA) images of retina is high (area under the receiver-operator characteristic curve AUC ~ 0.81). Bland-Altman analysis gave exceptionally low

SUBMITTER: Sharma P 

PROVIDER: S-EPMC9122907 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature