Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/141547
Title: Automated artefacts detection for OCT-Angiography images using deep learning
Authors: Quek, Kenny Jun Hao
Keywords: Engineering::Computer science and engineering::Computing methodologies::Artificial intelligence
Engineering::Mechanical engineering
Issue Date: 2020
Publisher: Nanyang Technological University
Project: B057
Abstract: Optical Coherence Tomography Angiography (OCT-Angiography) is a recent non-invasive imaging technique which enables visualization of microvasculature in the eye. There is increasing interest in the use of OCT-Angiography for disease studies and diagnosis. However, interpretation of OCT-Angiography can be affected by localized artefacts which only degrades image quality in a focal region of the image. This study presents a Defect Detection System (DDS), capable of automatic identification of artefacts in an OCT-Angiography image. Three convolutional neural network (CNN) architectures (VGG-16, VGG-19, ResNet-50) from the ImageNet classification were used to train the automated classifier using transfer learning. Results show that VGG-19 obtained the highest accuracy of 99.52% compared to the other networks. The results are promising for the use of DDS for automated OCT-Angiography image quality assessment.
URI: https://hdl.handle.net/10356/141547
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

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