Damped Least-Squares Recurrent Deep Neural Learning Classification For Glaucoma Detection

Raja, P. M. Siva; Ramanan, Kavita

doi:10.1109/icdse47409.2019.8971799

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Machine Learning/statistical Modeling-based Ai Classifiers A...1

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2023

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Cited by 4 publications

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“…Using multi-class classification (healthy, suspected glaucoma, and glaucoma), they provided the metrics of precision and recall which were 0.76 and 0.72 respectively. Raja and Ramanan proposed the use of Damped Least-Squares Recurrent Deep Neural Learning Classification (DL-RNL) ( 77 ). The classification was performed on the output layer using soft sign activation functions resulting in an accuracy of 89% however, no other performance metrics were specified.…”

Section: Machine Learning/statistical Modeling-based Ai Classifiers A...mentioning

confidence: 99%

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Coan¹,

Williams²,

Venkatesh³

et al. 2023

Survey of Ophthalmology

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Section: Machine Learning/statistical Modeling-based Ai Classifiers A...mentioning

confidence: 99%

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Coan¹,

Williams²,

Venkatesh³

et al. 2023

Survey of Ophthalmology

View full text Add to dashboard Cite

A Review of Deep Learning Techniques for Glaucoma Detection

Guergueb

Akhloufi

2023

SN COMPUT. SCI.

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Artificial intelligence in glaucoma: opportunities, challenges, and future directions

Huang,

Islam,

Akter

et al. 2023

BioMed Eng OnLine

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Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.

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Damped Least-Squares Recurrent Deep Neural Learning Classification For Glaucoma Detection

Cited by 4 publications

References 11 publications

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review

A Review of Deep Learning Techniques for Glaucoma Detection

Artificial intelligence in glaucoma: opportunities, challenges, and future directions

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