Re: Wang et al.: Machine learning models for diagnosing glaucoma from retinal nerve fiber layer thickness maps (Ophthalmology Glaucoma. 2019;2:422–428)

Odaibo, Stephen G.

doi:10.1016/j.ogla.2020.03.002

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…Researchers develop a machine learning predictive model that can select the five data features of the patients 1) visual field test,2) a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, 3) a general examination with 4) an intraocular pressure (IOP) measurement and 5) fundus photography. Finally, they used support vector machine (SVM), C5.0, random forest (RF), and XGboost algorithmsto test the predicted model [1].The researchers developed different prediction models based on deep learning techniques and use image data for prediction [2][3][4][5][6]. Also the traditional machine learning models areused for glaucoma prediction [7][8][9].Researchers comprehensively reviewed in their different articles about glaucoma, its types, cause, effect, and possible treatments.…”

Section: Related Workmentioning

confidence: 99%

Detection of Glucoma and Diabetes through Image Processing and Machine Learning Approaches

Bandyopadhyay¹,

Bose²,

Dutta³

et al. 2021

Preprint

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In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

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