Emulating Hyperspectral and Narrow-Band Imaging for Deep-Learning-Driven Gastrointestinal Disorder Detection in Wireless Capsule Endoscopy Article Swipe
Chu‐Kuang Chou
,
Kun-Hua Lee
,
Riya Karmakar
,
Arvind Mukundan
,
Prashant M. Gade
,
Devansh Gupta
,
Chang‐Chao Su
,
Tsung‐Hsien Chen
,
Chou‐Yuan Ko
,
Hsiang‐Chen Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12090953
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/bioengineering12090953
Diagnosing gastrointestinal disorders (GIDs) remains a significant challenge, particularly when relying on wireless capsule endoscopy (WCE), which lacks advanced imaging enhancements like Narrow Band Imaging (NBI). To address this, we propose a novel framework, the Spectrum-Aided Vision Enhancer (SAVE), especially designed to transform standard white light (WLI) endoscopic images into spectrally enriched representations that emulate both hyperspectral imaging (HSI) and NBI formats. By leveraging color calibration through the Macbeth Color Checker, gamma correction, CIE 1931 XYZ transformation, and principal component analysis (PCA), SAVE reconstructs detailed spectral information from conventional RGB inputs. Performance was evaluated using the Kvasir-v2 dataset, which includes 6490 annotated images spanning eight GI-related categories. Deep learning models like Inception-Net V3, MobileNetV2, MobileNetV3, and AlexNet were trained on both original WLI- and SAVE-enhanced images. Among these, MobileNetV2 achieved an F1-score of 96% for polyp classification using SAVE, and AlexNet saw a notable increase in average accuracy to 84% when applied to enhanced images. Image quality assessment showed high structural similarity (SSIM scores of 93.99% for Olympus endoscopy and 90.68% for WCE), confirming the fidelity of the spectral transformations. Overall, the SAVE framework offers a practical, software-based enhancement strategy that significantly improves diagnostic accuracy in GI imaging, with strong implications for low-cost, non-invasive diagnostics using capsule endoscopy systems.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/bioengineering12090953
- https://www.mdpi.com/2306-5354/12/9/953/pdf?version=1756976918
- OA Status
- gold
- References
- 44
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4414004973
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4414004973Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/bioengineering12090953Digital Object Identifier
- Title
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Emulating Hyperspectral and Narrow-Band Imaging for Deep-Learning-Driven Gastrointestinal Disorder Detection in Wireless Capsule EndoscopyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-09-04Full publication date if available
- Authors
-
Chu‐Kuang Chou, Kun-Hua Lee, Riya Karmakar, Arvind Mukundan, Prashant M. Gade, Devansh Gupta, Chang‐Chao Su, Tsung‐Hsien Chen, Chou‐Yuan Ko, Hsiang‐Chen WangList of authors in order
- Landing page
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https://doi.org/10.3390/bioengineering12090953Publisher landing page
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https://www.mdpi.com/2306-5354/12/9/953/pdf?version=1756976918Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2306-5354/12/9/953/pdf?version=1756976918Direct OA link when available
- Concepts
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Capsule endoscopy, Hyperspectral imaging, Narrow-band imaging, Artificial intelligence, Endoscopy, Computer science, Medicine, Computer vision, Radiology, Biomedical engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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44Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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