The Neural Network of One-Dimensional Convolution-An Example of the Diagnosis of Diabetic Retinopathy
Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the most important manifestation of diabetic microangiopathy and is also one of the most common complications in people with diabetes. At present...
Uloženo v:
| Vydáno v: | IEEE access Ročník 7; s. 69657 - 69666 |
|---|---|
| Hlavní autor: | |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Piscataway
IEEE
2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Témata: | |
| ISSN: | 2169-3536, 2169-3536 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the most important manifestation of diabetic microangiopathy and is also one of the most common complications in people with diabetes. At present, the diagnosis of diabetic retinal complications mainly depends on the pictures for diagnosis. The fundus images are the main ways to diagnose retinal diseases at present, but the diagnosis process is complicated. Based on this, this paper uses the electronic medical record information of 301 hospitalized patients with diabetes from 2009 to 2013, mainly using the diabetes diagnostic data, diabetes glycosylation data and diabetes biochemical test data, the depth of learning methods and medical diabetes combined with the application of convolution Neural Network Method (CNN) to build a diagnostic model, and thus draw the diagnosis. The main contribution of this study is twofold: 1) In this paper, we apply the CNN method to one-dimensional unrelated data sets and solve the problem of how to do one-dimensional irrelevant data convolution. 2) In this paper, the CNN model is combined with the BN layer to prevent the dispersion of the gradient, speed up the training speed and improve the accuracy of the model. In addition, this model incorporates an adaptive learning rate algorithm and optimizes the model. The experiments show that this method can achieve a training accuracy of 99.85% and a testing accuracy of 97.56%, which is more than 2% higher than that of using logistic regression. The model methods involved in this study can not only be used for the diagnosis of diabetic retinopathy, but also for the diagnosis of other diseases, such as chronic kidney disease, cardiovascular, and cerebrovascular diseases |
|---|---|
| AbstractList | Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the most important manifestation of diabetic microangiopathy and is also one of the most common complications in people with diabetes. At present, the diagnosis of diabetic retinal complications mainly depends on the pictures for diagnosis. The fundus images are the main ways to diagnose retinal diseases at present, but the diagnosis process is complicated. Based on this, this paper uses the electronic medical record information of 301 hospitalized patients with diabetes from 2009 to 2013, mainly using the diabetes diagnostic data, diabetes glycosylation data and diabetes biochemical test data, the depth of learning methods and medical diabetes combined with the application of convolution Neural Network Method (CNN) to build a diagnostic model, and thus draw the diagnosis. The main contribution of this study is twofold: 1) In this paper, we apply the CNN method to one-dimensional unrelated data sets and solve the problem of how to do one-dimensional irrelevant data convolution. 2) In this paper, the CNN model is combined with the BN layer to prevent the dispersion of the gradient, speed up the training speed and improve the accuracy of the model. In addition, this model incorporates an adaptive learning rate algorithm and optimizes the model. The experiments show that this method can achieve a training accuracy of 99.85% and a testing accuracy of 97.56%, which is more than 2% higher than that of using logistic regression. The model methods involved in this study can not only be used for the diagnosis of diabetic retinopathy, but also for the diagnosis of other diseases, such as chronic kidney disease, cardiovascular, and cerebrovascular diseases Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the most important manifestation of diabetic microangiopathy and is also one of the most common complications in people with diabetes. At present, the diagnosis of diabetic retinal complications mainly depends on the pictures for diagnosis. The fundus images are the main ways to diagnose retinal diseases at present, but the diagnosis process is complicated. Based on this, this paper uses the electronic medical record information of 301 hospitalized patients with diabetes from 2009 to 2013, mainly using the diabetes diagnostic data, diabetes glycosylation data and diabetes biochemical test data, the depth of learning methods and medical diabetes combined with the application of convolution Neural Network Method (CNN) to build a diagnostic model, and thus draw the diagnosis. The main contribution of this study is twofold: 1) In this paper, we apply the CNN method to one-dimensional unrelated data sets and solve the problem of how to do one-dimensional irrelevant data convolution. 2) In this paper, the CNN model is combined with the BN layer to prevent the dispersion of the gradient, speed up the training speed and improve the accuracy of the model. In addition, this model incorporates an adaptive learning rate algorithm and optimizes the model. The experiments show that this method can achieve a training accuracy of 99.85% and a testing accuracy of 97.56%, which is more than 2% higher than that of using logistic regression. The model methods involved in this study can not only be used for the diagnosis of diabetic retinopathy, but also for the diagnosis of other diseases, such as chronic kidney disease, cardiovascular, and cerebrovascular diseases. |
| Author | Sun, Yunlei |
| Author_xml | – sequence: 1 givenname: Yunlei orcidid: 0000-0003-3745-6899 surname: Sun fullname: Sun, Yunlei email: sunyunlei@upc.edu.cn organization: College of Computer & Communication Engineering, China University of Petroleum (East China), Qingdao, China |
| BookMark | eNp9Uctu1DAUtVCRKKVf0E0k1hn8jr0cpQNUqqhEy9pyMteth4w92J5C_x6HFIRY4MV9-Zyjq3teo5MQAyB0QfCKEKzfrft-c3u7opjoFdVEakpfoFNai5YJJk_-ql-h85x3uD5VR6I7RXD3AM0nOCY71VS-x_S1ia65CdBe-j2E7GOoX30Mj3E6ltq169Bsftj9YYIZWSr_0tv7ELPP86A2AxQ_Np9rDPFgy8PTG_TS2SnD-XM-Q1_eb-76j-31zYerfn3djhyr0g5MDbrTGBRzzvGtpNh1Sim65U4AV6MWWo2UYKGckpYNgwZOmVWSM2s5ZmfoatHdRrszh-T3Nj2ZaL35NYjp3thUd5vAjANQah0fMHVcMztsKceCEzdKrZXmVevtonVI8dsRcjG7eEz1GNlQLoSkvJOyovSCGlPMOYEzoy92vlNJ1k-GYDObZBaTzGySeTapctk_3N8b_591sbA8APxhqI4Ijgn7CRtnnrQ |
| CODEN | IAECCG |
| CitedBy_id | crossref_primary_10_1016_j_media_2020_101742 crossref_primary_10_3233_JIFS_220772 crossref_primary_10_2478_jaiscr_2025_0009 crossref_primary_10_3390_s22051803 crossref_primary_10_1007_s11042_023_16449_9 crossref_primary_10_1080_21681163_2021_2002190 crossref_primary_10_1109_ACCESS_2022_3142925 crossref_primary_10_3233_HIS_220004 crossref_primary_10_3390_diagnostics13081439 crossref_primary_10_1155_2022_7902786 crossref_primary_10_1016_j_optlastec_2025_113284 crossref_primary_10_1109_ACCESS_2020_2976149 crossref_primary_10_1007_s00371_022_02489_z crossref_primary_10_1007_s10462_022_10185_6 crossref_primary_10_1109_ACCESS_2020_3016782 crossref_primary_10_1007_s11042_024_19766_9 crossref_primary_10_1080_03091902_2020_1791986 crossref_primary_10_1109_ACCESS_2021_3074458 |
| Cites_doi | 10.1007/s12539-009-0016-2 10.1145/2808492.2808524 10.3115/v1/D14-1181 10.1109/ACII.2015.7344603 10.1016/j.procs.2017.08.193 10.1190/1.1443281 10.1109/ICCV.2015.178 10.1109/CVPR.2015.7298594 10.1007/978-3-319-46493-0_38 10.1109/ICIP.2015.7351374 |
| ContentType | Journal Article |
| Copyright | Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| Copyright_xml | – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019 |
| DBID | 97E ESBDL RIA RIE AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D DOA |
| DOI | 10.1109/ACCESS.2019.2916922 |
| DatabaseName | IEEE All-Society Periodicals Package (ASPP) 2005–Present IEEE Xplore Open Access Journals IEEE All-Society Periodicals Package (ASPP) 1998–Present IEL CrossRef Computer and Information Systems Abstracts Electronics & Communications Abstracts Engineered Materials Abstracts METADEX Technology Research Database Materials Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef Materials Research Database Engineered Materials Abstracts Technology Research Database Computer and Information Systems Abstracts – Academic Electronics & Communications Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Advanced Technologies Database with Aerospace METADEX Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Materials Research Database |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| EISSN | 2169-3536 |
| EndPage | 69666 |
| ExternalDocumentID | oai_doaj_org_article_cbe22af4b02f493abd240541fc699894 10_1109_ACCESS_2019_2916922 8715401 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: Fundamental Research Funds for the Central Universities: Automated machine learning for diabetes big data grantid: 18CX02019A |
| GroupedDBID | 0R~ 4.4 5VS 6IK 97E AAJGR ABAZT ABVLG ACGFS ADBBV AGSQL ALMA_UNASSIGNED_HOLDINGS BCNDV BEFXN BFFAM BGNUA BKEBE BPEOZ EBS EJD ESBDL GROUPED_DOAJ IPLJI JAVBF KQ8 M43 M~E O9- OCL OK1 RIA RIE RNS AAYXX CITATION 7SC 7SP 7SR 8BQ 8FD JG9 JQ2 L7M L~C L~D |
| ID | FETCH-LOGICAL-c408t-b38b9790e83fff4d620f78882d4f5e48c9598c21058f86a3bb9e423a8643aa403 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 27 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000471758100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2169-3536 |
| IngestDate | Fri Oct 03 12:52:21 EDT 2025 Sat Nov 01 14:32:57 EDT 2025 Sat Nov 29 03:57:35 EST 2025 Tue Nov 18 21:51:49 EST 2025 Wed Aug 27 06:00:42 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c408t-b38b9790e83fff4d620f78882d4f5e48c9598c21058f86a3bb9e423a8643aa403 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ORCID | 0000-0003-3745-6899 |
| OpenAccessLink | https://ieeexplore.ieee.org/document/8715401 |
| PQID | 2455624766 |
| PQPubID | 4845423 |
| PageCount | 10 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_cbe22af4b02f493abd240541fc699894 crossref_citationtrail_10_1109_ACCESS_2019_2916922 crossref_primary_10_1109_ACCESS_2019_2916922 proquest_journals_2455624766 ieee_primary_8715401 |
| PublicationCentury | 2000 |
| PublicationDate | 20190000 2019-00-00 20190101 2019-01-01 |
| PublicationDateYYYYMMDD | 2019-01-01 |
| PublicationDate_xml | – year: 2019 text: 20190000 |
| PublicationDecade | 2010 |
| PublicationPlace | Piscataway |
| PublicationPlace_xml | – name: Piscataway |
| PublicationTitle | IEEE access |
| PublicationTitleAbbrev | Access |
| PublicationYear | 2019 |
| Publisher | IEEE The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher_xml | – name: IEEE – name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| References | ref12 ref14 baek (ref4) 2003; 2 vijayan (ref10) 2016 ref2 ref17 ref18 ref8 zhang (ref7) 2015 xu (ref13) 2014; 2 ref9 ref3 ref6 cao (ref1) 2016; 31 ref5 zeiler (ref11) 2013 krizhevsky (ref15) 2012; 60 simonyan (ref16) 2014; abs 1409 1556 |
| References_xml | – start-page: 818 year: 2013 ident: ref11 article-title: Visualizing and understanding convolutional networks publication-title: Proc Eur Conf Comput Vis – volume: 2 start-page: 1790 year: 2014 ident: ref13 article-title: Deep convolutional neural network for image deconvolution publication-title: Proc Adv Neural Inf Process Syst – ident: ref9 doi: 10.1007/s12539-009-0016-2 – start-page: 649 year: 2015 ident: ref7 article-title: Character-level convolutional networks for text classification publication-title: Proc Adv Neural Inf Process Syst – start-page: 122 year: 2016 ident: ref10 article-title: Prediction and diagnosis of diabetes mellitus-A machine learning approach publication-title: Proc IEEE Recent Adv Intell Comput Syst (RAICS) – volume: 60 start-page: 2012 year: 2012 ident: ref15 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun ACM – volume: 31 start-page: 33 year: 2016 ident: ref1 article-title: Prediction and comparison of risk of retinopathy with type 2 diabetes mellitus based on Logistic regression and random forest algorithm publication-title: China Medical Equipment – ident: ref2 doi: 10.1145/2808492.2808524 – ident: ref6 doi: 10.3115/v1/D14-1181 – ident: ref5 doi: 10.1109/ACII.2015.7344603 – ident: ref8 doi: 10.1016/j.procs.2017.08.193 – ident: ref14 doi: 10.1190/1.1443281 – volume: 2 start-page: 1219 year: 2003 ident: ref4 article-title: Image edge detection using adaptive morphology Meyer wavelet-CNN publication-title: Proc Int Joint Conf Neural Netw – volume: abs 1409 1556 year: 2014 ident: ref16 article-title: Very deep convolutional networks for large-scale image recognition publication-title: Comput Sci – ident: ref12 doi: 10.1109/ICCV.2015.178 – ident: ref17 doi: 10.1109/CVPR.2015.7298594 – ident: ref18 doi: 10.1007/978-3-319-46493-0_38 – ident: ref3 doi: 10.1109/ICIP.2015.7351374 |
| SSID | ssj0000816957 |
| Score | 2.320936 |
| Snippet | Diabetes is a serious threat to health development, because diabetes is a disease that caused most other diseases (complications). Diabetic retinopathy is the... |
| SourceID | doaj proquest crossref ieee |
| SourceType | Open Website Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 69657 |
| SubjectTerms | Accuracy Adaptive algorithms Adaptive learning adaptive learning rate Artificial neural networks batch normalization Convolution convolutional neural network Deconvolution Diabetes Diabetic mellitus Diabetic retinopathy Diagnosis Diagnostic systems Electronic health records Feature extraction Kidney diseases Machine learning Model accuracy Neural networks Regression models Retinopathy Training |
| SummonAdditionalLinks | – databaseName: DOAJ Directory of Open Access Journals dbid: DOA link: http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1RaxQxEA5S-qAPYlvF01by0EfXZrPZbObxem3pQzlFVPoWkmwCB7InvWup_96ZTXpcEeqLr2Gyu5mZZL5ZJt8wdlyDTwk3UdXr3lSqF23la0xWjFO-jsJ1zXhD7sdVN5-b62v4stXqi2rCMj1wVtxJ8FFKl5QXMilonO8xBrWqTkEDkYfT6Ss62EqmxjPY1BrartAM1QJOprMZrohqueCTREwEUj4KRSNjf2mx8te5PAabi1fsZUGJfJq_bo89i8M-e7HFHXjAIhqYE7UGys1zLTdfJv55iNUZMfZntg0-Ww53xbuq6cDP7x3RAZMkIj9-lgvtFisayMUxi8C_0jXoJfUq_v2afb84_za7rErPhCooYdaVb4yHDkQ0TUpJ9VqKRFmu7FVqozIBWjAB87zWJKNd4z1ERFTOIDJxTonmDdsZlkN8yzi0DrEGzmgAkxTduQ50wuAWGqNkn2DC5IP6bCiE4tTX4qcdEwsBNuvcks5t0fmEfdxM-pX5NJ4WPyW7bESJDHscQBexxUXsv1xkwg7IqpuHYI6IOLWesMMHK9uycVdWqhYRoeq0fvc_Xv2ePafl5H82h2xnfXMbj9huuFsvVjcfRp_9A2Nr650 priority: 102 providerName: Directory of Open Access Journals |
| Title | The Neural Network of One-Dimensional Convolution-An Example of the Diagnosis of Diabetic Retinopathy |
| URI | https://ieeexplore.ieee.org/document/8715401 https://www.proquest.com/docview/2455624766 https://doaj.org/article/cbe22af4b02f493abd240541fc699894 |
| Volume | 7 |
| WOSCitedRecordID | wos000471758100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: DOA dateStart: 20130101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2169-3536 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000816957 issn: 2169-3536 databaseCode: M~E dateStart: 20130101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwELVKxQEO5aOgLpTKB45NmziOP47LdisOsCAEqDfLdmxpJZSg7raCC7-dGduNqKiQuESRNY6SPDt-48y8IeR1o12MMImqXvSq4n3dVa4BZ0VZ7ppQW9mmDLmv7-RqpS4u9McdcjzlwoQQUvBZOMHT9C-_H_0VbpWdArkHggG-zj0pRc7VmvZTsICE7mQRFmpqfTpfLOAZMHpLnzBgQZqxW4tP0ugvRVX--hKn5eX80f_d2GOyV2gknWfcn5CdMDwlD_8QF9wnAUYARe0NsFvlYG86RvphCNUZSvpnOQ66GIfrMvyq-UCXPyzqBaMlUEN6liPx1htsyNEza08_YZ70iMWMfz4jX86Xnxdvq1JUofK8VtvKtcppqeug2hgj7wWrI7rBrOexC1x53WnlwRHsVFTCts7pAJTLKqAu1vK6fU52h3EIB4TqzgIZgR6tBi9GSCu1iLD6-VZx1kc9I-zmbRtfFMex8MU3kzyPWpsMkUGITIFoRo6nTt-z4Ma_zd8gjJMpqmWnBsDHlMlnvAuM2chdzSLXrXU98JiON9ELjQL0M7KPmE4XKXDOyOHNoDBlZm8M4x1QRi6FeHF3r5fkAd5g3qY5JLvby6vwitz319v15vIo-fxwfP9reZQG8G8kUuqx |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1Nb9QwEB1VLRJwKB-lYtsCPnBsWsdxEvu43bYqYlkQKqg3y3ZsaSWUoO62gn_fmcSNQCAkbpFlR07eOH7jzLwBeJtrFyMuoqypGpXJhpeZy9FZUVa6PHBbF32G3Nd5vVioqyv9aQMOx1yYEEIffBaO6LL_l990_oaOyo6R3CPBQF9niypnpWyt8USFSkjosk7SQjnXx9PZDJ-C4rf0kUAepIX4bfvpVfpTWZU_vsX9BnP-5P-m9hS2E5Fk0wH5Z7AR2ufw-Bd5wR0IaAOM1Dew32II92ZdZB_bkJ2SqP8gyMFmXXubDDCbtuzshyXFYOqJ5JCdDrF4yxU1DPEzS88-U6Z0R-WMf76AL-dnl7OLLJVVyLzkap25Qjldax5UEWOUTSV4JEdYNDKWQSqvS608uoKliqqyhXM6IOmyCsmLtZIXu7DZdm14CUyXFukIjig0-jFVbWtdRdz_fKGkaKKegLh_28YnzXEqffHN9L4H12aAyBBEJkE0gcNx0PdBcuPf3U8IxrEr6WX3DYiPScvPeBeEsFE6LqLUhXUNMplS5tFXmiToJ7BDmI43SXBO4ODeKExa2ysjZImkUdZVtff3UW_g4cXlh7mZv1u834dHNNnh0OYANtfXN-EVPPC36-Xq-nVvwHdzouvU |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=The+Neural+Network+of+One-Dimensional+Convolution-An+Example+of+the+Diagnosis+of+Diabetic+Retinopathy&rft.jtitle=IEEE+access&rft.au=Sun%2C+Yunlei&rft.date=2019&rft.pub=IEEE&rft.eissn=2169-3536&rft.volume=7&rft.spage=69657&rft.epage=69666&rft_id=info:doi/10.1109%2FACCESS.2019.2916922&rft.externalDocID=8715401 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2169-3536&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2169-3536&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2169-3536&client=summon |