A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification
Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless...
Ausführliche Beschreibung
Autor*in: |
Vij, Richa [verfasserIn] |
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Englisch |
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2023 |
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Multiclass DR severity classification |
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Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2023), 22 vom: 03. März, Seite 34847-34884 |
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Übergeordnetes Werk: |
volume:82 ; year:2023 ; number:22 ; day:03 ; month:03 ; pages:34847-34884 |
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DOI / URN: |
10.1007/s11042-023-14963-4 |
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OLC2145380787 |
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520 | |a Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. | ||
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10.1007/s11042-023-14963-4 doi (DE-627)OLC2145380787 (DE-He213)s11042-023-14963-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Vij, Richa verfasserin aut A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification Arora, Sakshi aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 22 vom: 03. März, Seite 34847-34884 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 https://doi.org/10.1007/s11042-023-14963-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 22 03 03 34847-34884 |
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10.1007/s11042-023-14963-4 doi (DE-627)OLC2145380787 (DE-He213)s11042-023-14963-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Vij, Richa verfasserin aut A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification Arora, Sakshi aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 22 vom: 03. März, Seite 34847-34884 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 https://doi.org/10.1007/s11042-023-14963-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 22 03 03 34847-34884 |
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10.1007/s11042-023-14963-4 doi (DE-627)OLC2145380787 (DE-He213)s11042-023-14963-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Vij, Richa verfasserin aut A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification Arora, Sakshi aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 22 vom: 03. März, Seite 34847-34884 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 https://doi.org/10.1007/s11042-023-14963-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 22 03 03 34847-34884 |
allfieldsGer |
10.1007/s11042-023-14963-4 doi (DE-627)OLC2145380787 (DE-He213)s11042-023-14963-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Vij, Richa verfasserin aut A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification Arora, Sakshi aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 22 vom: 03. März, Seite 34847-34884 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 https://doi.org/10.1007/s11042-023-14963-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 22 03 03 34847-34884 |
allfieldsSound |
10.1007/s11042-023-14963-4 doi (DE-627)OLC2145380787 (DE-He213)s11042-023-14963-4-p DE-627 ger DE-627 rakwb eng 070 004 VZ Vij, Richa verfasserin aut A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification Arora, Sakshi aut Enthalten in Multimedia tools and applications Springer US, 1995 82(2023), 22 vom: 03. März, Seite 34847-34884 (DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 1380-7501 nnns volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 https://doi.org/10.1007/s11042-023-14963-4 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW AR 82 2023 22 03 03 34847-34884 |
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a novel deep transfer learning based computerized diagnostic systems for multi-class imbalanced diabetic retinopathy severity classification |
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A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
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Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstractGer |
Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
abstract_unstemmed |
Abstract Diabetic Retinopathy (DR) is a retinal condition that leads to gradual degeneration of the retina and eventual blindness, so early detection and evaluation of disease development are essential for effective treatment. Retinography is the most prevalent method for diagnosing DR; nevertheless, manual diagnosis is time-consuming and unpleasant. Deep Learning (DL) based algorithms have shown potential as a diagnostic tool for DR, achieving performance comparable to human image evaluation. The goal of this study is to develop Deep Transfer Learning-based Computerized Diagnostic Systems (DTL-CDS) for Multiclass DR Severity Classification (MCDR) by modifying and comparing deep inductive transfer learning (DITL) models (Inception V3, ResNet34, EfficientNet B0, VGG16, Xception). The four main objectives are i) pre-processing and balancing the imbalanced data labels, ii) proposing modified DITL model to extract features using global average pooling layer to avoid overfitting and reduce losses using leaky ReLU. The final classification uses a softmax layer to automatically classify Diabetic Retinopathy severity stages using IDRiD dataset, (iii) comparing different base and modified DITL models to existing work, and (iv) evaluating the robustness of proposed model using various performance metrics. Comprehensive analysis of MCDR between the proposed and the existing work shows that the proposed approach outperforms the state-of-the-art, with 99.36% accuracy, 0.986 precision, 0.986 recall, 0.986 F1-score, 0.997 AUC-ROC, 0.9902 AUC. The experimental results show the enhancement in diagnosis performance and modified DITL results in robust and reliable computer-aided diagnosis systems to aid specialists in proper detection of DR severity stages by reducing human errors and reducing costs. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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