ArticleDiagnostics (Basel, Switzerland)2024
Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
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- Image Dataset of Microfauna in Activated Sludge.Scientific data · 2025Article
- Deep learning for digital pathology: A critical overview of methodological framework.Journal of pathology informatics · 2025Review
- Transforming Endoscopic Image Classification with Spectrum-Aided Vision for Early and Accurate Cancer Identification.Diagnostics (Basel, Switzerland) · 2025Article
- Hyperspectral Imaging for Enhanced Skin Cancer Classification Using Machine Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Sine oculis homeobox homolog family function in gastrointestinal cancer: Progression and comprehensive analysis.World journal of clinical oncology · 2025Review
- Article
- Article
- Enhanced Detection of Colon Diseases via a Fused Deep Learning Model with an Auxiliary Fusion Layer and Residual Blocks on Endoscopic Images.Current medical imaging · 2025Article
- Automated Blood Cell Detection and Classification in Microscopic Images Using YOLOv11 and Optimized Weights.Diagnostics (Basel, Switzerland) · 2024Article
- The Impact of Tumor Stage and Histopathology on Survival Outcomes in Esophageal Cancer Patients over the Past Decade.Medical sciences (Basel, Switzerland) · 2024Article
- Advancing hyperspectral imaging and machine learning tools toward clinical adoption in tissue diagnostics: A comprehensive review.APL bioengineering · 2024Review
- Using Pathomics-Based Model for Predicting Positive Surgical Margins in Patients with Esophageal Squamous Cell Carcinoma: A Comparative Study of Decision Tree and Nomogram.International journal of general medicine · 2024Article
- Development and validation of esophageal cancer prediction model based on DeepSurv model: A cohort study.Science progressArticle
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9 authors.
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Abstract
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%,
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