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Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model
Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that a...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778546/ https://www.ncbi.nlm.nih.gov/pubmed/36554021 http://dx.doi.org/10.3390/healthcare10122497 |
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author | Kashyap, Ramgopal Nair, Rajit Gangadharan, Syam Machinathu Parambil Botto-Tobar, Miguel Farooq, Saadia Rizwan, Ali |
author_facet | Kashyap, Ramgopal Nair, Rajit Gangadharan, Syam Machinathu Parambil Botto-Tobar, Miguel Farooq, Saadia Rizwan, Ali |
author_sort | Kashyap, Ramgopal |
collection | PubMed |
description | Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model’s outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use. |
format | Online Article Text |
id | pubmed-9778546 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97785462022-12-23 Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model Kashyap, Ramgopal Nair, Rajit Gangadharan, Syam Machinathu Parambil Botto-Tobar, Miguel Farooq, Saadia Rizwan, Ali Healthcare (Basel) Article Glaucoma is prominent in a variety of nations, with the United States and Europe being two of the most famous. Glaucoma now affects around 78 million people throughout the world (2020). By the year 2040, it is expected that there will be 111.8 million cases of glaucoma worldwide. In countries that are still building enough healthcare infrastructure to cope with glaucoma, the ailment is misdiagnosed nine times out of ten. To aid in the early diagnosis of glaucoma, the creation of a detection system is necessary. In this work, the researchers propose using a technology known as deep learning to identify and predict glaucoma before symptoms appear. The glaucoma dataset is used in this deep learning algorithm that has been proposed for analyzing glaucoma images. To get the required results when using deep learning principles for the job of segmenting the optic cup, pretrained transfer learning models are integrated with the U-Net architecture. For feature extraction, the DenseNet-201 deep convolution neural network (DCNN) is used. The DCNN approach is used to determine whether a person has glaucoma. The fundamental goal of this line of research is to recognize glaucoma in retinal fundus images, which will aid in assessing whether a patient has the condition. Because glaucoma can affect the model in both positive and negative ways, the model’s outcome might be either positive or negative. Accuracy, precision, recall, specificity, the F-measure, and the F-score are some of the metrics used in the model evaluation process. An extra comparison study is performed as part of the process of establishing whether the suggested model is accurate. The findings are compared to convolution neural network classification methods based on deep learning. When used for training, the suggested model has an accuracy of 98.82 percent and an accuracy of 96.90 percent when used for testing. All assessments show that the new paradigm that has been proposed is more successful than the one that is currently in use. MDPI 2022-12-09 /pmc/articles/PMC9778546/ /pubmed/36554021 http://dx.doi.org/10.3390/healthcare10122497 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Kashyap, Ramgopal Nair, Rajit Gangadharan, Syam Machinathu Parambil Botto-Tobar, Miguel Farooq, Saadia Rizwan, Ali Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
title | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
title_full | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
title_fullStr | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
title_full_unstemmed | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
title_short | Glaucoma Detection and Classification Using Improved U-Net Deep Learning Model |
title_sort | glaucoma detection and classification using improved u-net deep learning model |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9778546/ https://www.ncbi.nlm.nih.gov/pubmed/36554021 http://dx.doi.org/10.3390/healthcare10122497 |
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