DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only
<p>Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of deve...
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| مؤلفون آخرون: | , , |
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2021
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| _version_ | 1864513560456986624 |
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| author | Mohammad Tariqul Islam (7854059) |
| author2 | Hamada R. H. Al-Absi (16726299) Essam A. Ruagh (16896480) Tanvir Alam (638619) |
| author2_role | author author author |
| author_facet | Mohammad Tariqul Islam (7854059) Hamada R. H. Al-Absi (16726299) Essam A. Ruagh (16896480) Tanvir Alam (638619) |
| author_role | author |
| dc.creator.none.fl_str_mv | Mohammad Tariqul Islam (7854059) Hamada R. H. Al-Absi (16726299) Essam A. Ruagh (16896480) Tanvir Alam (638619) |
| dc.date.none.fl_str_mv | 2021-01-18T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2021.3052477 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/DiaNet_A_Deep_Learning_Based_Architecture_to_Diagnose_Diabetes_Using_Retinal_Images_Only/24049356 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning Diabetes Retina Medical diagnostic imaging Retinopathy Task analysis Feature extraction Convolutional neural network Deep learning Machine learning Qatar Qatar Biobank (QBB) Magrabi Eye, Dental and Ear Centre |
| dc.title.none.fl_str_mv | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p>Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing diabetes are primarily reliant upon clinical biomarkers. In this article, we propose a novel deep learning architecture to predict if a person has diabetes or not from a photograph of his/her retina. Using a relatively small-sized dataset, we develop a multi-stage convolutional neural network (CNN)-based model DiaNet that can reach an accuracy level of over 84% on this task, and in doing so, successfully identifies the regions on the retina images that contribute to its decision-making process, as corroborated by the medical experts in the field. This is the first study that highlights the distinguishing capability of the retinal images for diabetes patients in the Qatari population to the best of our knowledge. Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. In addition, our study reveals that retinal images may contain prognosis markers for diabetes and other comorbidities like hypertension and ischemic heart disease. The results led us to believe that the inclusion of retinal images into the clinical setup for the diagnosis of diabetes is warranted in the near future.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3052477" target="_blank">https://dx.doi.org/10.1109/access.2021.3052477</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_9db6e81b6e75a1ab8e8547c1bea37332 |
| identifier_str_mv | 10.1109/access.2021.3052477 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24049356 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images OnlyMohammad Tariqul Islam (7854059)Hamada R. H. Al-Absi (16726299)Essam A. Ruagh (16896480)Tanvir Alam (638619)Biomedical and clinical sciencesClinical sciencesEngineeringBiomedical engineeringInformation and computing sciencesMachine learningDiabetesRetinaMedical diagnostic imagingRetinopathyTask analysisFeature extractionConvolutional neural networkDeep learningMachine learningQatarQatar Biobank (QBB)Magrabi Eye, Dental and Ear Centre<p>Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing diabetes are primarily reliant upon clinical biomarkers. In this article, we propose a novel deep learning architecture to predict if a person has diabetes or not from a photograph of his/her retina. Using a relatively small-sized dataset, we develop a multi-stage convolutional neural network (CNN)-based model DiaNet that can reach an accuracy level of over 84% on this task, and in doing so, successfully identifies the regions on the retina images that contribute to its decision-making process, as corroborated by the medical experts in the field. This is the first study that highlights the distinguishing capability of the retinal images for diabetes patients in the Qatari population to the best of our knowledge. Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. In addition, our study reveals that retinal images may contain prognosis markers for diabetes and other comorbidities like hypertension and ischemic heart disease. The results led us to believe that the inclusion of retinal images into the clinical setup for the diagnosis of diabetes is warranted in the near future.</p><h2>Other Information</h2><p>Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/legalcode" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2021.3052477" target="_blank">https://dx.doi.org/10.1109/access.2021.3052477</a></p>2021-01-18T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2021.3052477https://figshare.com/articles/journal_contribution/DiaNet_A_Deep_Learning_Based_Architecture_to_Diagnose_Diabetes_Using_Retinal_Images_Only/24049356CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/240493562021-01-18T00:00:00Z |
| spellingShingle | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only Mohammad Tariqul Islam (7854059) Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning Diabetes Retina Medical diagnostic imaging Retinopathy Task analysis Feature extraction Convolutional neural network Deep learning Machine learning Qatar Qatar Biobank (QBB) Magrabi Eye, Dental and Ear Centre |
| status_str | publishedVersion |
| title | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| title_full | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| title_fullStr | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| title_full_unstemmed | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| title_short | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| title_sort | DiaNet: A Deep Learning Based Architecture to Diagnose Diabetes Using Retinal Images Only |
| topic | Biomedical and clinical sciences Clinical sciences Engineering Biomedical engineering Information and computing sciences Machine learning Diabetes Retina Medical diagnostic imaging Retinopathy Task analysis Feature extraction Convolutional neural network Deep learning Machine learning Qatar Qatar Biobank (QBB) Magrabi Eye, Dental and Ear Centre |