Title of the dissertation: “Machine learning: automated detection of glaucoma diagnosis using fundus images”
Literature review: review of the literature in the automated detection of glaucoma diagnosis using fundus images (machine learning).
The paper will have contain:
– the early glaucoma detection review (non machine methods – specifying the types of retinal screening) to automated glaucoma detection using machine learning, summarising the most significant works in the literature (to latest innovations in the glaucoma detection – as example https://www.straitstimes.com/singapore/parenting-education/nus-students-win-top-international-james-dyson-award-with-glaucoma )
– different methodologies used for automated glaucoma detection;
– previous works for optic disc and optic cup segmentation, along with glaucoma classification and retinal image synthesis methods;
– deep learning and implications in the glaucoma detection.
In the same time, the paper must refer to previous works with the insights of the results and look for gaps in the specified field.
The paper can contain formulas, vectors, tables or images, as long as the word count from the tables is not going to count towards the final word count