Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks Article Swipe
Chu‐Kuang Chou
,
Riya Karmakar
,
Yu-Ming Tsao
,
Lim Wei Jie
,
Arvind Mukundan
,
Chien‐Wei Huang
,
Tsung‐Hsien Chen
,
Chau-Yuan Ko
,
Hsiang‐Chen Wang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14111129
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics14111129
The early detection of esophageal cancer presents a substantial difficulty, which contributes to its status as a primary cause of cancer-related fatalities. This study used You Only Look Once (YOLO) frameworks, specifically YOLOv5 and YOLOv8, to predict and detect early-stage EC by using a dataset sourced from the Division of Gastroenterology and Hepatology, Ditmanson Medical Foundation, Chia-Yi Christian Hospital. The dataset comprised 2741 white-light images (WLI) and 2741 hyperspectral narrowband images (HSI-NBI). They were divided into 60% training, 20% validation, and 20% test sets to facilitate robust detection. The images were produced using a conversion method called the spectrum-aided vision enhancer (SAVE). This algorithm can transform a WLI into an NBI without requiring a spectrometer or spectral head. The main goal was to identify dysplasia and squamous cell carcinoma (SCC). The model’s performance was evaluated using five essential metrics: precision, recall, F1-score, mAP, and the confusion matrix. The experimental results demonstrated that the HSI model exhibited improved learning capabilities for SCC characteristics compared with the original RGB images. Within the YOLO framework, YOLOv5 outperformed YOLOv8, indicating that YOLOv5’s design possessed superior feature-learning skills. The YOLOv5 model, when used in conjunction with HSI-NBI, demonstrated the best performance. It achieved a precision rate of 85.1% (CI95: 83.2–87.0%, p < 0.01) in diagnosing SCC and an F1-score of 52.5% (CI95: 50.1–54.9%, p < 0.01) in detecting dysplasia. The results of these figures were much better than those of YOLOv8. YOLOv8 achieved a precision rate of 81.7% (CI95: 79.6–83.8%, p < 0.01) and an F1-score of 49.4% (CI95: 47.0–51.8%, p < 0.05). The YOLOv5 model with HSI demonstrated greater performance than other models in multiple scenarios. This difference was statistically significant, suggesting that the YOLOv5 model with HSI significantly improved detection capabilities.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics14111129
- https://www.mdpi.com/2075-4418/14/11/1129/pdf?version=1716977882
- OA Status
- gold
- Cited By
- 12
- References
- 52
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399139872
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399139872Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics14111129Digital Object Identifier
- Title
-
Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO FrameworksWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-29Full publication date if available
- Authors
-
Chu‐Kuang Chou, Riya Karmakar, Yu-Ming Tsao, Lim Wei Jie, Arvind Mukundan, Chien‐Wei Huang, Tsung‐Hsien Chen, Chau-Yuan Ko, Hsiang‐Chen WangList of authors in order
- Landing page
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https://doi.org/10.3390/diagnostics14111129Publisher landing page
- PDF URL
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https://www.mdpi.com/2075-4418/14/11/1129/pdf?version=1716977882Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2075-4418/14/11/1129/pdf?version=1716977882Direct OA link when available
- Concepts
-
Esophageal cancer, Enhancer, Computer science, Computer vision, Computational biology, Cancer, Biology, Medicine, Internal medicine, Genetics, Gene, Gene expressionTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 5Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.spectrometer | 114 |
| abstract_inverted_index.47.0–51.8%, | 254 |
| abstract_inverted_index.50.1–54.9%, | 217 |
| abstract_inverted_index.79.6–83.8%, | 244 |
| abstract_inverted_index.83.2–87.0%, | 204 |
| abstract_inverted_index.capabilities. | 287 |
| abstract_inverted_index.hyperspectral | 68 |
| abstract_inverted_index.significantly | 284 |
| abstract_inverted_index.statistically | 275 |
| abstract_inverted_index.cancer-related | 20 |
| abstract_inverted_index.spectrum-aided | 98 |
| abstract_inverted_index.characteristics | 161 |
| abstract_inverted_index.Gastroenterology | 50 |
| abstract_inverted_index.feature-learning | 181 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| corresponding_author_ids | https://openalex.org/A5109725861, https://openalex.org/A5052246876 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 9 |
| corresponding_institution_ids | https://openalex.org/I148099254, https://openalex.org/I4210097004, https://openalex.org/I4210118607 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.4699999988079071 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile.value | 0.98431427 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |