An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means
The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ e...
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| Published in | BioMed research international Vol. 2021; no. 1 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Hindawi
2021
John Wiley & Sons, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2314-6133 2314-6141 2314-6141 |
| DOI | 10.1155/2021/5597222 |
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| Abstract | The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images. |
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| AbstractList | The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients’ eyes. In addition, this study integrated two methods, including fuzzy C‐means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients’ retinopathy eyes. As clearly confirmed by the obtained results, the GA‐FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images. The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients' eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients' retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images.The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing so, this study presented the growth region algorithm for the aim of diagnosing diabetes, considering the angiography images of the patients' eyes. In addition, this study integrated two methods, including fuzzy C-means (FCM) and genetic algorithm (GA) to predict the retinopathy in diabetic patients from angiography images. The developed algorithm was applied to a total of 224 images of patients' retinopathy eyes. As clearly confirmed by the obtained results, the GA-FCM method outperformed the hand method regarding the selection of initial points. The proposed method showed 0.78 sensitivity. The comparison of the fuzzy fitness function in GA with other techniques revealed that the approach introduced in this study is more applicable to the Jaccard index since it could offer the lowest Jaccard distance and, at the same time, the highest Jaccard values. The results of the analysis demonstrated that the proposed method was efficient and effective to predict the retinopathy in diabetic patients from angiography images. |
| Audience | Academic |
| Author | Ranjbarzadeh, Ramin Dadkhah, Amir Hussein Ghoushchi, Saeid Jafarzadeh Pourasad, Yaghoub Bendechache, Malika |
| AuthorAffiliation | 4 School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland 1 Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran 2 Department of Telecommunications Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran 3 Department of Electrical Engineering, Urmia University of Technology, Urmia, Iran |
| AuthorAffiliation_xml | – name: 1 Faculty of Industrial Engineering, Urmia University of Technology, Urmia, Iran – name: 4 School of Computing, Faculty of Engineering and Computing, Dublin City University, Ireland – name: 2 Department of Telecommunications Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran – name: 3 Department of Electrical Engineering, Urmia University of Technology, Urmia, Iran |
| Author_xml | – sequence: 1 givenname: Saeid Jafarzadeh orcidid: 0000-0003-3665-9010 surname: Ghoushchi fullname: Ghoushchi, Saeid Jafarzadeh organization: Faculty of Industrial EngineeringUrmia University of TechnologyUrmiaIranuut.ac.ir – sequence: 2 givenname: Ramin orcidid: 0000-0001-7065-9060 surname: Ranjbarzadeh fullname: Ranjbarzadeh, Ramin organization: Department of Telecommunications EngineeringFaculty of EngineeringUniversity of GuilanRashtIranguilan.ac.ir – sequence: 3 givenname: Amir Hussein orcidid: 0000-0001-6310-7460 surname: Dadkhah fullname: Dadkhah, Amir Hussein organization: Faculty of Industrial EngineeringUrmia University of TechnologyUrmiaIranuut.ac.ir – sequence: 4 givenname: Yaghoub orcidid: 0000-0003-1487-1651 surname: Pourasad fullname: Pourasad, Yaghoub organization: Department of Electrical EngineeringUrmia University of TechnologyUrmiaIranuut.ac.ir – sequence: 5 givenname: Malika orcidid: 0000-0003-0069-1860 surname: Bendechache fullname: Bendechache, Malika organization: School of ComputingFaculty of Engineering and ComputingDublin City UniversityIrelanddcu.ie |
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| Copyright | Copyright © 2021 Saeid Jafarzadeh Ghoushchi et al. COPYRIGHT 2021 John Wiley & Sons, Inc. Copyright © 2021 Saeid Jafarzadeh Ghoushchi et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 Copyright © 2021 Saeid Jafarzadeh Ghoushchi et al. 2021 |
| Copyright_xml | – notice: Copyright © 2021 Saeid Jafarzadeh Ghoushchi et al. – notice: COPYRIGHT 2021 John Wiley & Sons, Inc. – notice: Copyright © 2021 Saeid Jafarzadeh Ghoushchi et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0 – notice: Copyright © 2021 Saeid Jafarzadeh Ghoushchi et al. 2021 |
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| Snippet | The present study is developed a new approach using a computer diagnostic method to diagnosing diabetic diseases with the use of fluorescein images. In doing... |
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| SubjectTerms | Algorithms Analysis Angiography Automation Care and treatment Clustering (Computers) Diabetes Diabetes mellitus Diabetic retinopathy Diabetics Diagnosis Disease Fluorescein Genetic algorithms Medical imaging Medical imaging equipment Methods Neural networks Patients Prevention Retina Retinopathy Risk factors Visual impairment |
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| Title | An Extended Approach to Predict Retinopathy in Diabetic Patients Using the Genetic Algorithm and Fuzzy C-Means |
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