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Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images
Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is ch...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264426/ https://www.ncbi.nlm.nih.gov/pubmed/37311794 http://dx.doi.org/10.1038/s41598-023-35197-2 |
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author | El-Den, Niveen Nasr Naglah, Ahmed Elsharkawy, Mohamed Ghazal, Mohammed Alghamdi, Norah Saleh Sandhu, Harpal Mahdi, Hani El-Baz, Ayman |
author_facet | El-Den, Niveen Nasr Naglah, Ahmed Elsharkawy, Mohamed Ghazal, Mohammed Alghamdi, Norah Saleh Sandhu, Harpal Mahdi, Hani El-Baz, Ayman |
author_sort | El-Den, Niveen Nasr |
collection | PubMed |
description | Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient’s condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively. |
format | Online Article Text |
id | pubmed-10264426 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-102644262023-06-15 Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images El-Den, Niveen Nasr Naglah, Ahmed Elsharkawy, Mohamed Ghazal, Mohammed Alghamdi, Norah Saleh Sandhu, Harpal Mahdi, Hani El-Baz, Ayman Sci Rep Article Age-related Macular Degeneration (AMD), a retinal disease that affects the macula, can be caused by aging abnormalities in number of different cells and tissues in the retina, retinal pigment epithelium, and choroid, leading to vision loss. An advanced form of AMD, called exudative or wet AMD, is characterized by the ingrowth of abnormal blood vessels beneath or into the macula itself. The diagnosis is confirmed by either fundus auto-fluorescence imaging or optical coherence tomography (OCT) supplemented by fluorescein angiography or OCT angiography without dye. Fluorescein angiography, the gold standard diagnostic procedure for AMD, involves invasive injections of fluorescent dye to highlight retinal vasculature. Meanwhile, patients can be exposed to life-threatening allergic reactions and other risks. This study proposes a scale-adaptive auto-encoder-based model integrated with a deep learning model that can detect AMD early by automatically analyzing the texture patterns in color fundus imaging and correlating them to the vasculature activity in the retina. Moreover, the proposed model can automatically distinguish between AMD grades assisting in early diagnosis and thus allowing for earlier treatment of the patient’s condition, slowing the disease and minimizing its severity. Our model features two main blocks, the first is an auto-encoder-based network for scale adaption, and the second is a convolutional neural network (CNN) classification network. Based on a conducted set of experiments, the proposed model achieves higher diagnostic accuracy compared to other models with accuracy, sensitivity, and specificity that reach 96.2%, 96.2%, and 99%, respectively. Nature Publishing Group UK 2023-06-13 /pmc/articles/PMC10264426/ /pubmed/37311794 http://dx.doi.org/10.1038/s41598-023-35197-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article El-Den, Niveen Nasr Naglah, Ahmed Elsharkawy, Mohamed Ghazal, Mohammed Alghamdi, Norah Saleh Sandhu, Harpal Mahdi, Hani El-Baz, Ayman Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
title | Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
title_full | Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
title_fullStr | Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
title_full_unstemmed | Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
title_short | Scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
title_sort | scale-adaptive model for detection and grading of age-related macular degeneration from color retinal fundus images |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10264426/ https://www.ncbi.nlm.nih.gov/pubmed/37311794 http://dx.doi.org/10.1038/s41598-023-35197-2 |
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