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A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique
ABSTRACT: Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neur...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225981/ https://www.ncbi.nlm.nih.gov/pubmed/35545738 http://dx.doi.org/10.1007/s11517-022-02564-6 |
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author | AbdelMaksoud, Eman Barakat, Sherif Elmogy, Mohammed |
author_facet | AbdelMaksoud, Eman Barakat, Sherif Elmogy, Mohammed |
author_sort | AbdelMaksoud, Eman |
collection | PubMed |
description | ABSTRACT: Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model’s hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , 0.883, and 3.5m respectively. The experiments show promising results as compared with other systems. GRAPHICAL ABSTRACT: [Image: see text] |
format | Online Article Text |
id | pubmed-9225981 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-92259812022-06-25 A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique AbdelMaksoud, Eman Barakat, Sherif Elmogy, Mohammed Med Biol Eng Comput Original Article ABSTRACT: Diabetic retinopathy (DR) is a serious disease that may cause vision loss unawares without any alarm. Therefore, it is essential to scan and audit the DR progress continuously. In this respect, deep learning techniques achieved great success in medical image analysis. Deep convolution neural network (CNN) architectures are widely used in multi-label (ML) classification. It helps in diagnosing normal and various DR grades: mild, moderate, and severe non-proliferative DR (NPDR) and proliferative DR (PDR). DR grades are formulated by appearing multiple DR lesions simultaneously on the color retinal fundus images. Many lesion types have various features that are difficult to segment and distinguished by utilizing conventional and hand-crafted methods. Therefore, the practical solution is to utilize an effective CNN model. In this paper, we present a novel hybrid, deep learning technique, which is called E-DenseNet. We integrated EyeNet and DenseNet models based on transfer learning. We customized the traditional EyeNet by inserting the dense blocks and optimized the resulting hybrid E-DensNet model’s hyperparameters. The proposed system based on the E-DenseNet model can accurately diagnose healthy and different DR grades from various small and large ML color fundus images. We trained and tested our model on four different datasets that were published from 2006 to 2019. The proposed system achieved an average accuracy (ACC), sensitivity (SEN), specificity (SPE), Dice similarity coefficient (DSC), the quadratic Kappa score (QKS), and the calculation time (T) in minutes (m) equal [Formula: see text] , [Formula: see text] , [Formula: see text] , [Formula: see text] , 0.883, and 3.5m respectively. The experiments show promising results as compared with other systems. GRAPHICAL ABSTRACT: [Image: see text] Springer Berlin Heidelberg 2022-05-11 2022 /pmc/articles/PMC9225981/ /pubmed/35545738 http://dx.doi.org/10.1007/s11517-022-02564-6 Text en © The Author(s) 2022 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 | Original Article AbdelMaksoud, Eman Barakat, Sherif Elmogy, Mohammed A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
title | A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
title_full | A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
title_fullStr | A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
title_full_unstemmed | A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
title_short | A computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
title_sort | computer-aided diagnosis system for detecting various diabetic retinopathy grades based on a hybrid deep learning technique |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9225981/ https://www.ncbi.nlm.nih.gov/pubmed/35545738 http://dx.doi.org/10.1007/s11517-022-02564-6 |
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