Revolutionizing diabetic eye disease detection: retinal image analysis with cutting-edge deep learning techniques.

ABSTRACT: Globally, glaucoma is a leading cause of visual impairment and vision loss, emphasizing the critical need for early diagnosis and intervention. This research explores the application of deep learning for automated glaucoma diagnosis using retinal fundus photographs. We introduce a novel cross-sectional optic nerve head (ONH) feature derived from optical coherence tomography (OCT) images to enhance existing diagnostic procedures. Our approach leverages deep learning to automatically detect key optic disc characteristics, eliminating the need for manual feature engineering. The deep learning classifier then categorizes images as normal or abnormal, streamlining the diagnostic process. Deep learning techniques have proven effective in classifying and segmenting retinal fundus images, enabling

SUBMITTER: D B 

PROVIDER: S-EPMC11623275 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature