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
Gespeichert in:
| Autor*in: |
Vij, Richa [verfasserIn] |
|---|
| Format: |
Artikel |
|---|---|
| Sprache: |
Englisch |
| Erschienen: |
2023 |
|---|
| Schlagwörter: |
Multiclass DR severity classification |
|---|
| 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. |
|---|
| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Springer US, 1995, 82(2023), 22 vom: 03. März, Seite 34847-34884 |
|---|---|
| Übergeordnetes Werk: |
volume:82 ; year:2023 ; number:22 ; day:03 ; month:03 ; pages:34847-34884 |
| Links: |
|---|
| DOI / URN: |
10.1007/s11042-023-14963-4 |
|---|
| Katalog-ID: |
OLC2145380787 |
|---|
| LEADER | 01000naa a22002652 4500 | ||
|---|---|---|---|
| 001 | OLC2145380787 | ||
| 003 | DE-627 | ||
| 005 | 20240118104230.0 | ||
| 007 | tu | ||
| 008 | 240118s2023 xx ||||| 00| ||eng c | ||
| 024 | 7 | |a 10.1007/s11042-023-14963-4 |2 doi | |
| 035 | |a (DE-627)OLC2145380787 | ||
| 035 | |a (DE-He213)s11042-023-14963-4-p | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 082 | 0 | 4 | |a 070 |a 004 |q VZ |
| 100 | 1 | |a Vij, Richa |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
| 264 | 1 | |c 2023 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
| 338 | |a Band |b nc |2 rdacarrier | ||
| 500 | |a © 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. | ||
| 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. | ||
| 650 | 4 | |a Diabetic retinopathy | |
| 650 | 4 | |a Multiclass DR severity classification | |
| 650 | 4 | |a Deep inductive transfer learning model | |
| 650 | 4 | |a Global average pooling layer | |
| 650 | 4 | |a IDRiD dataset | |
| 650 | 4 | |a Severity stage classification | |
| 700 | 1 | |a Arora, Sakshi |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Multimedia tools and applications |d Springer US, 1995 |g 82(2023), 22 vom: 03. März, Seite 34847-34884 |w (DE-627)189064145 |w (DE-600)1287642-2 |w (DE-576)052842126 |x 1380-7501 |7 nnns |
| 773 | 1 | 8 | |g volume:82 |g year:2023 |g number:22 |g day:03 |g month:03 |g pages:34847-34884 |
| 856 | 4 | 1 | |u https://doi.org/10.1007/s11042-023-14963-4 |z lizenzpflichtig |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a SYSFLAG_A | ||
| 912 | |a GBV_OLC | ||
| 912 | |a SSG-OLC-MAT | ||
| 912 | |a SSG-OLC-BUB | ||
| 912 | |a SSG-OLC-MKW | ||
| 951 | |a AR | ||
| 952 | |d 82 |j 2023 |e 22 |b 03 |c 03 |h 34847-34884 | ||
| author_variant |
r v rv s a sa |
|---|---|
| matchkey_str |
article:13807501:2023----::nvleprnfrerigaecmueiedansissesomlilsiblnedaeir |
| hierarchy_sort_str |
2023 |
| publishDate |
2023 |
| allfields |
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 |
| spelling |
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 |
| allfields_unstemmed |
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 |
| language |
English |
| source |
Enthalten in Multimedia tools and applications 82(2023), 22 vom: 03. März, Seite 34847-34884 volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 |
| sourceStr |
Enthalten in Multimedia tools and applications 82(2023), 22 vom: 03. März, Seite 34847-34884 volume:82 year:2023 number:22 day:03 month:03 pages:34847-34884 |
| format_phy_str_mv |
Article |
| institution |
findex.gbv.de |
| topic_facet |
Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification |
| dewey-raw |
070 |
| isfreeaccess_bool |
false |
| container_title |
Multimedia tools and applications |
| authorswithroles_txt_mv |
Vij, Richa @@aut@@ Arora, Sakshi @@aut@@ |
| publishDateDaySort_date |
2023-03-03T00:00:00Z |
| hierarchy_top_id |
189064145 |
| dewey-sort |
270 |
| id |
OLC2145380787 |
| language_de |
englisch |
| fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2145380787</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118104230.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-023-14963-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2145380787</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-023-14963-4-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vij, Richa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diabetic retinopathy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiclass DR severity classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep inductive transfer learning model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Global average pooling layer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IDRiD dataset</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Severity stage classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arora, Sakshi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">82(2023), 22 vom: 03. März, Seite 34847-34884</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:82</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:22</subfield><subfield code="g">day:03</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:34847-34884</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-023-14963-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">82</subfield><subfield code="j">2023</subfield><subfield code="e">22</subfield><subfield code="b">03</subfield><subfield code="c">03</subfield><subfield code="h">34847-34884</subfield></datafield></record></collection>
|
| author |
Vij, Richa |
| spellingShingle |
Vij, Richa ddc 070 misc Diabetic retinopathy misc Multiclass DR severity classification misc Deep inductive transfer learning model misc Global average pooling layer misc IDRiD dataset misc Severity stage classification A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
| authorStr |
Vij, Richa |
| ppnlink_with_tag_str_mv |
@@773@@(DE-627)189064145 |
| format |
Article |
| dewey-ones |
070 - News media, journalism & publishing 004 - Data processing & computer science |
| delete_txt_mv |
keep |
| author_role |
aut aut |
| collection |
OLC |
| remote_str |
false |
| illustrated |
Not Illustrated |
| issn |
1380-7501 |
| topic_title |
070 004 VZ A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification Diabetic retinopathy Multiclass DR severity classification Deep inductive transfer learning model Global average pooling layer IDRiD dataset Severity stage classification |
| topic |
ddc 070 misc Diabetic retinopathy misc Multiclass DR severity classification misc Deep inductive transfer learning model misc Global average pooling layer misc IDRiD dataset misc Severity stage classification |
| topic_unstemmed |
ddc 070 misc Diabetic retinopathy misc Multiclass DR severity classification misc Deep inductive transfer learning model misc Global average pooling layer misc IDRiD dataset misc Severity stage classification |
| topic_browse |
ddc 070 misc Diabetic retinopathy misc Multiclass DR severity classification misc Deep inductive transfer learning model misc Global average pooling layer misc IDRiD dataset misc Severity stage classification |
| format_facet |
Aufsätze Gedruckte Aufsätze |
| format_main_str_mv |
Text Zeitschrift/Artikel |
| carriertype_str_mv |
nc |
| hierarchy_parent_title |
Multimedia tools and applications |
| hierarchy_parent_id |
189064145 |
| dewey-tens |
070 - News media, journalism & publishing 000 - Computer science, knowledge & systems |
| hierarchy_top_title |
Multimedia tools and applications |
| isfreeaccess_txt |
false |
| familylinks_str_mv |
(DE-627)189064145 (DE-600)1287642-2 (DE-576)052842126 |
| title |
A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
| ctrlnum |
(DE-627)OLC2145380787 (DE-He213)s11042-023-14963-4-p |
| title_full |
A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
| author_sort |
Vij, Richa |
| journal |
Multimedia tools and applications |
| journalStr |
Multimedia tools and applications |
| lang_code |
eng |
| isOA_bool |
false |
| dewey-hundreds |
000 - Computer science, information & general works |
| recordtype |
marc |
| publishDateSort |
2023 |
| contenttype_str_mv |
txt |
| container_start_page |
34847 |
| author_browse |
Vij, Richa Arora, Sakshi |
| container_volume |
82 |
| class |
070 004 VZ |
| format_se |
Aufsätze |
| author-letter |
Vij, Richa |
| doi_str_mv |
10.1007/s11042-023-14963-4 |
| dewey-full |
070 004 |
| title_sort |
a novel deep transfer learning based computerized diagnostic systems for multi-class imbalanced diabetic retinopathy severity classification |
| title_auth |
A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
| abstract |
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. |
| collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-BUB SSG-OLC-MKW |
| container_issue |
22 |
| title_short |
A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification |
| url |
https://doi.org/10.1007/s11042-023-14963-4 |
| remote_bool |
false |
| author2 |
Arora, Sakshi |
| author2Str |
Arora, Sakshi |
| ppnlink |
189064145 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s11042-023-14963-4 |
| up_date |
2024-07-04T02:56:49.601Z |
| _version_ |
1803615532074663936 |
| fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2145380787</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118104230.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11042-023-14963-4</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2145380787</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11042-023-14963-4-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">070</subfield><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Vij, Richa</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A novel deep transfer learning based computerized diagnostic Systems for Multi-class imbalanced diabetic retinopathy severity classification</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© 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.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Diabetic retinopathy</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiclass DR severity classification</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Deep inductive transfer learning model</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Global average pooling layer</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">IDRiD dataset</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Severity stage classification</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Arora, Sakshi</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Multimedia tools and applications</subfield><subfield code="d">Springer US, 1995</subfield><subfield code="g">82(2023), 22 vom: 03. März, Seite 34847-34884</subfield><subfield code="w">(DE-627)189064145</subfield><subfield code="w">(DE-600)1287642-2</subfield><subfield code="w">(DE-576)052842126</subfield><subfield code="x">1380-7501</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:82</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:22</subfield><subfield code="g">day:03</subfield><subfield code="g">month:03</subfield><subfield code="g">pages:34847-34884</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s11042-023-14963-4</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MKW</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">82</subfield><subfield code="j">2023</subfield><subfield code="e">22</subfield><subfield code="b">03</subfield><subfield code="c">03</subfield><subfield code="h">34847-34884</subfield></datafield></record></collection>
|
| score |
7.4011145 |