Cardiomegaly Detection on Chest Radiographs: Segmentation Versus Classification Article Swipe
Ecem Sogancioglu
,
Keelin Murphy
,
Erdi Çallı
,
Ernst T. Scholten
,
Steven Schalekamp
,
Bram van Ginneken
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2995567
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/access.2020.2995567
In this study, we investigate the detection of cardiomegaly on frontal chest radiographs through two alternative deep-learning approaches - via anatomical segmentation and via image-level classification. We used the publicly available ChestX-ray14 dataset, and obtained heart and lung segmentation annotations for 778 chest radiographs for the development of the segmentation-based approach. The classification-based method was trained with 65k standard chest radiographs with image-level labels. For both approaches, the best models were found through hyperparameter searches where architectural, learning, and regularization related parameters were optimized systematically. The resulting models were tested on a set of 367 held-out images for which cardiomegaly annotations were hand-labeled by two independent expert radiologists. Sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) were calculated. The performance of the segmentation-based system with an AUC of 0.977 is significantly better for classifying cardiomegaly than the classification-based model which achieved an AUC of 0.941. Only the segmentation-based model achieved comparable performance to an independent expert reader (AUC of 0.978). We conclude that the segmentation-based model requires 100 times fewer annotated chest radiographs to achieve a substantially better performance, while also producing more interpretable results.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2020.2995567
- https://ieeexplore.ieee.org/ielx7/6287639/8948470/09096290.pdf
- OA Status
- gold
- Cited By
- 53
- References
- 55
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3028577876
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3028577876Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2020.2995567Digital Object Identifier
- Title
-
Cardiomegaly Detection on Chest Radiographs: Segmentation Versus ClassificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-01-01Full publication date if available
- Authors
-
Ecem Sogancioglu, Keelin Murphy, Erdi Çallı, Ernst T. Scholten, Steven Schalekamp, Bram van GinnekenList of authors in order
- Landing page
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https://doi.org/10.1109/access.2020.2995567Publisher landing page
- PDF URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09096290.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/8948470/09096290.pdfDirect OA link when available
- Concepts
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Segmentation, Receiver operating characteristic, Hyperparameter, Artificial intelligence, Computer science, Radiography, Pattern recognition (psychology), Image segmentation, Radiology, Machine learning, MedicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
53Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 14, 2023: 12, 2022: 13, 2021: 7Per-year citation counts (last 5 years)
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55Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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