On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

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Title

On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images

Content partner

The University of Auckland Library

Collection

ResearchSpace@Auckland

Description

Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity ...

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Format

Research paper

Research format

Journal article

Date created

2023-03

Creator

Karn, Prakash Kumar / Abdulla, Waleed H

URL

https://hdl.handle.net/2292/67092

Related subjects

OCT / deep learning / fundus / machine learning / 40 Engineering / 4003 Biomedical Engineering / Eye Disease and Disorders of Vision / Biomedical Imaging / Neurosciences / Bioengineering / 4 Detection, screening and diagnosis / 4.1 Discovery and preclinical testing of markers and technologies / 4.2 Evaluation of markers and technologies / Eye / Science & Technology / Life Sciences & Biomedicine / Technology / Biotechnology & Applied Microbiology / Engineering, Biomedical / Engineering

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