Problem‐Oriented Strategy for Diabetic Retinopathy Identification.
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| Title: | Problem‐Oriented Strategy for Diabetic Retinopathy Identification. |
|---|---|
| Authors: | Hadef, Mahdi1 (AUTHOR) hadef.mahdi@gmail.com, Boulahia, Said Yacine1 (AUTHOR), Amamra, Abdenour1 (AUTHOR) |
| Source: | International Journal of Imaging Systems & Technology. Sep2025, Vol. 35 Issue 5, p1-22. 22p. |
| Subjects: | Diabetic retinopathy, Diagnosis, Machine learning, High resolution imaging, Symptoms, Retinal imaging, Deep learning |
| Abstract: | Diabetic retinopathy is a prevalent and sight‐threatening complication of diabetes that affects individuals worldwide. Effectively addressing this condition requires adapting approaches to the specific characteristics of retinal images. Existing works often tackle the diagnostic challenge without focusing on a specific aspect. In contrast, our study introduces a new problem‐oriented strategy that addresses key gaps in diabetic retinopathy using three novel, tailored approaches. First, to address the underexploitation of high‐resolution retinal images, we propose a resolution‐preserving, data‐based approach that employs patch‐based analysis without downscaling while also mitigating data scarcity and imbalance. Second, inspired by real‐world clinical practice, we develop a symptoms‐based approach that explicitly segments multiple key pathological indicators (blood vessels, exudates, and microaneurysms) and then uses them to guide the classification network. Third, we propose a hierarchical approach that decomposes the multi‐stage classification task into multiple hierarchical binary classifications, enabling more specialized feature learning and informed decision‐making across different severity levels. Evaluations on both EyePACS and APTOS benchmark datasets showcased superior performance, surpassing or matching contemporary state‐of‐the‐art results. These outcomes demonstrate the effectiveness of our proposed approaches and underscore the strategy's potential to improve diabetic retinopathy diagnosis. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188295100 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Problem‐Oriented Strategy for Diabetic Retinopathy Identification. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hadef%2C+Mahdi%22">Hadef, Mahdi</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> hadef.mahdi@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Boulahia%2C+Said+Yacine%22">Boulahia, Said Yacine</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Amamra%2C+Abdenour%22">Amamra, Abdenour</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Imaging+Systems+%26+Technology%22">International Journal of Imaging Systems & Technology</searchLink>. Sep2025, Vol. 35 Issue 5, p1-22. 22p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Diabetic+retinopathy%22">Diabetic retinopathy</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22High+resolution+imaging%22">High resolution imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Symptoms%22">Symptoms</searchLink><br /><searchLink fieldCode="DE" term="%22Retinal+imaging%22">Retinal imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Diabetic retinopathy is a prevalent and sight‐threatening complication of diabetes that affects individuals worldwide. Effectively addressing this condition requires adapting approaches to the specific characteristics of retinal images. Existing works often tackle the diagnostic challenge without focusing on a specific aspect. In contrast, our study introduces a new problem‐oriented strategy that addresses key gaps in diabetic retinopathy using three novel, tailored approaches. First, to address the underexploitation of high‐resolution retinal images, we propose a resolution‐preserving, data‐based approach that employs patch‐based analysis without downscaling while also mitigating data scarcity and imbalance. Second, inspired by real‐world clinical practice, we develop a symptoms‐based approach that explicitly segments multiple key pathological indicators (blood vessels, exudates, and microaneurysms) and then uses them to guide the classification network. Third, we propose a hierarchical approach that decomposes the multi‐stage classification task into multiple hierarchical binary classifications, enabling more specialized feature learning and informed decision‐making across different severity levels. Evaluations on both EyePACS and APTOS benchmark datasets showcased superior performance, surpassing or matching contemporary state‐of‐the‐art results. These outcomes demonstrate the effectiveness of our proposed approaches and underscore the strategy's potential to improve diabetic retinopathy diagnosis. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Imaging Systems & Technology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1002/ima.70216 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 22 StartPage: 1 Subjects: – SubjectFull: Diabetic retinopathy Type: general – SubjectFull: Diagnosis Type: general – SubjectFull: Machine learning Type: general – SubjectFull: High resolution imaging Type: general – SubjectFull: Symptoms Type: general – SubjectFull: Retinal imaging Type: general – SubjectFull: Deep learning Type: general Titles: – TitleFull: Problem‐Oriented Strategy for Diabetic Retinopathy Identification. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hadef, Mahdi – PersonEntity: Name: NameFull: Boulahia, Said Yacine – PersonEntity: Name: NameFull: Amamra, Abdenour IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 09 Text: Sep2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 08999457 Numbering: – Type: volume Value: 35 – Type: issue Value: 5 Titles: – TitleFull: International Journal of Imaging Systems & Technology Type: main |
| ResultId | 1 |
