Flowchart of the methodology of glaucoma detection and staging using machine learning techniques.

Flowchart of the methodology of glaucoma detection and staging using machine learning techniques.

<p>Flowchart of the methodology of glaucoma detection and staging using machine learning techniques.</p>

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Bibliographic Details
Main Author:	Nahida Akter (5348495) (author)
Other Authors:	Jack Gordon (20577163) (author), Sherry Li (10504385) (author), Mikki Poon (20577166) (author), Stuart Perry (17382675) (author), John Fletcher (3212946) (author), Thomas Chan (5858531) (author), Andrew White (92654) (author), Maitreyee Roy (10046389) (author)
Published:	2025
Subjects:	Medicine Cell Biology Biotechnology Space Science Biological Sciences not elsewhere classified visual field index cam visualization technique affected vf loss achieved high accuracy mills criteria using glaucomatous visual fields 146 glaucomatous eyes 2 vf images 265 numerical datasets dl model results automated dl model 0 %, recall two popular pre conventional global indices classifier performed best pd images demonstrated 8 %, precision precision glaucoma detection dl model trained highest f1 score augmented pd images trained dl models 265 pd plots four ml models performance using balanced dl model global indices classifier trained 9 %, glaucoma detection dl models trained images performed similarly augmented data 0 %. pd plots overall f1 f1 score glaucomatous groups ml models four groups investigated using stage glaucoma moderate glaucoma advanced glaucoma xlink "> random forest progression management pattern deviation original dataset mean deviation machine learning fold cross feature extraction deep learning classification performance assist clinicians advanced stage
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