An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm
Glaucoma, a neuro-degenerative eye disease, is the result of an increase in intraocular pressure inside the retina. It is the second-leading cause of blindness worldwide, and if an early diagnosis is not made, it can lead to total blindness. There is a critical need to develop a system that can work...
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| Format: | Article |
| Language: | English |
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EDP Sciences
2024-01-01
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| Series: | MATEC Web of Conferences |
| Subjects: | |
| Online Access: | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01108.pdf |
| _version_ | 1797248865125007360 |
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| author | Subbarayudu Yerragudipadu Vijendar Reddy Gurram Raj Kumar Masuram Aravind Naik Mudavath Nagini R.V.S.S. Singh Balpreet |
| author_facet | Subbarayudu Yerragudipadu Vijendar Reddy Gurram Raj Kumar Masuram Aravind Naik Mudavath Nagini R.V.S.S. Singh Balpreet |
| author_sort | Subbarayudu Yerragudipadu |
| collection | DOAJ |
| description | Glaucoma, a neuro-degenerative eye disease, is the result of an increase in intraocular pressure inside the retina. It is the second-leading cause of blindness worldwide, and if an early diagnosis is not made, it can lead to total blindness. There is a critical need to develop a system that can work well without a lot of equipment, qualified medical professionals, and requires less time about this core issue. This article provides a thorough examination of the main machine learning (ML) techniques employed in the processing of retinal images for the identification and diagnosis of glaucoma. Machine learning (ML) has been demonstrated to be a crucial technique for the development of computer-assisted technology. Machine learning (ML) techniques can be used to construct predictive models for the early diagnosis of glaucoma. Our objective is to develop a machine learning algorithm that can accurately forecast the likelihood of developing glaucoma using patient data. Ophthalmologists have also conducted a significant amount of secondary research over the years. Such characteristics emphasise the importance of ML while analysing retinal pictures. |
| first_indexed | 2024-04-24T20:21:22Z |
| format | Article |
| id | doaj.art-22e54cf77c9d439fa2708c899f17a256 |
| institution | Directory Open Access Journal |
| issn | 2261-236X |
| language | English |
| last_indexed | 2024-04-24T20:21:22Z |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | MATEC Web of Conferences |
| spelling | doaj.art-22e54cf77c9d439fa2708c899f17a2562024-03-22T08:05:18ZengEDP SciencesMATEC Web of Conferences2261-236X2024-01-013920110810.1051/matecconf/202439201108matecconf_icmed2024_01108An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigmSubbarayudu Yerragudipadu0Vijendar Reddy Gurram1Raj Kumar Masuram2Aravind Naik Mudavath3Nagini R.V.S.S.4Singh Balpreet5Department of AI&ML, KG Reddy College of Engineering and TechnologyDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of Information Technology, GRIETDepartment of IT, GRIETLovely Professional UniversityGlaucoma, a neuro-degenerative eye disease, is the result of an increase in intraocular pressure inside the retina. It is the second-leading cause of blindness worldwide, and if an early diagnosis is not made, it can lead to total blindness. There is a critical need to develop a system that can work well without a lot of equipment, qualified medical professionals, and requires less time about this core issue. This article provides a thorough examination of the main machine learning (ML) techniques employed in the processing of retinal images for the identification and diagnosis of glaucoma. Machine learning (ML) has been demonstrated to be a crucial technique for the development of computer-assisted technology. Machine learning (ML) techniques can be used to construct predictive models for the early diagnosis of glaucoma. Our objective is to develop a machine learning algorithm that can accurately forecast the likelihood of developing glaucoma using patient data. Ophthalmologists have also conducted a significant amount of secondary research over the years. Such characteristics emphasise the importance of ML while analysing retinal pictures.https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01108.pdfmachine learningglaucoma predictionretinafundus imagesartificial intelligencelogistic regression |
| spellingShingle | Subbarayudu Yerragudipadu Vijendar Reddy Gurram Raj Kumar Masuram Aravind Naik Mudavath Nagini R.V.S.S. Singh Balpreet An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm MATEC Web of Conferences machine learning glaucoma prediction retina fundus images artificial intelligence logistic regression |
| title | An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm |
| title_full | An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm |
| title_fullStr | An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm |
| title_full_unstemmed | An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm |
| title_short | An efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm |
| title_sort | efficient novel approach for glaucoma classification on retinal fundus images through machine learning paradigm |
| topic | machine learning glaucoma prediction retina fundus images artificial intelligence logistic regression |
| url | https://www.matec-conferences.org/articles/matecconf/pdf/2024/04/matecconf_icmed2024_01108.pdf |
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