Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas Article Swipe
Ayesha Jabbar
,
Shahid Naseem
,
Jianqiang Li
,
Tariq Mahmood
,
Muhammad Kashif Jabbar
,
Amjad Rehman
,
Tanzila Saba
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s44196-024-00520-w
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1007/s44196-024-00520-w
Diabetic retinopathy (DR) significantly burdens ophthalmic healthcare due to its wide prevalence and high diagnostic costs. Especially in remote areas with limited medical access, undetected DR cases are on the rise. Our study introduces an advanced deep transfer learning-based system for real-time DR detection using fundus cameras to address this. This research aims to develop an efficient and timely assistance system for DR patients, empowering them to manage their health better. The proposed system leverages fundus imaging to collect retinal images, which are then transmitted to the processing unit for effective disease severity detection and classification. Comprehensive reports guide subsequent medical actions based on the identified stage. The proposed system achieves real-time DR detection by utilizing deep transfer learning algorithms, specifically VGGNet. The system’s performance is rigorously evaluated, comparing its classification accuracy to previous research outcomes. The experimental results demonstrate the robustness of the proposed system, achieving an impressive 97.6% classification accuracy during the detection phase, surpassing the performance of existing approaches. Implementing the automated system in remote areas has transformed healthcare dynamics, enabling early, cost-effective DR diagnosis for millions. The system also streamlines patient prioritization, facilitating timely interventions for early-stage DR cases.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s44196-024-00520-w
- https://link.springer.com/content/pdf/10.1007/s44196-024-00520-w.pdf
- OA Status
- gold
- Cited By
- 28
- References
- 59
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399139471
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399139471Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s44196-024-00520-wDigital Object Identifier
- Title
-
Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote AreasWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-29Full publication date if available
- Authors
-
Ayesha Jabbar, Shahid Naseem, Jianqiang Li, Tariq Mahmood, Muhammad Kashif Jabbar, Amjad Rehman, Tanzila SabaList of authors in order
- Landing page
-
https://doi.org/10.1007/s44196-024-00520-wPublisher landing page
- PDF URL
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https://link.springer.com/content/pdf/10.1007/s44196-024-00520-w.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://link.springer.com/content/pdf/10.1007/s44196-024-00520-w.pdfDirect OA link when available
- Concepts
-
Fundus (uterus), Diabetic retinopathy, Retinal, Ophthalmology, Optometry, Computer science, Transfer of learning, Artificial intelligence, Retinopathy, Medicine, Diabetes mellitus, EndocrinologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
28Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 20, 2024: 8Per-year citation counts (last 5 years)
- References (count)
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59Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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