ArticleJournal of personalized medicine2022
Identification of Early Esophageal Cancer by Semantic Segmentation.
Article in Journal of personalized medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Recent Advances in Counterfeit Art, Document, Photo, Hologram, and Currency Detection Using Hyperspectral Imaging.Sensors (Basel, Switzerland) · 2022Pooled it
- Hyperspectral reconstruction of white light endoscopy images for enhanced segmentation of early esophageal lesions in patients.iScience · 2026Article
- Explainable machine learning for early diagnosis of esophageal cancer: A feature-enriched Light Gradient Boosting Machine framework with Shapley Additive Explanations and Local Interpretable Model-Agnostic Explanations interpretations.The Journal of international medical research · 2026Article
- Assessing deep learning models for multi-class upper endoscopic disease segmentation: A comprehensive comparative study.World journal of gastroenterology · 2025Article
- Current Role of Artificial Intelligence in the Management of Esophageal Cancer.Journal of clinical medicine · 2025Review
- Small intestinal bleeding prediction by spectral reconstruction through band selection.Journal of biomedical optics · 2025Article
- Precision Imaging for Early Detection of Esophageal Cancer.Bioengineering (Basel, Switzerland) · 2025Article
- Systematic Meta-Analysis of Computer-Aided Detection of Breast Cancer Using Hyperspectral Imaging.Bioengineering (Basel, Switzerland) · 2024Review
- Advancements in Hyperspectral Imaging and Computer-Aided Diagnostic Methods for the Enhanced Detection and Diagnosis of Head and Neck Cancer.Biomedicines · 2024Review
- Evaluation of Spectrum-Aided Visual Enhancer (SAVE) in Esophageal Cancer Detection Using YOLO Frameworks.Diagnostics (Basel, Switzerland) · 2024Article
- Design of risk prediction model for esophageal cancer based on machine learning approach.Heliyon · 2024Article
- Article
- A Review of Recent Advances in Computer-Aided Detection Methods Using Hyperspectral Imaging Engineering to Detect Skin Cancer.Cancers · 2023Review
- Assessment of hyperspectral imaging and CycleGAN-simulated narrowband techniques to detect early esophageal cancer.Scientific reports · 2023Article
- Article
- Artificial intelligence in esophageal cancer diagnosis and treatment: where are we now?-a narrative review.Annals of translational medicine · 2023Review
- Systematic meta-analysis of computer-aided detection to detect early esophageal cancer using hyperspectral imaging.Biomedical optics express · 2023Article
- Optical Identification of Diabetic Retinopathy Using Hyperspectral Imaging.Journal of personalized medicine · 2023Article
- Automatic Counterfeit Currency Detection Using a Novel Snapshot Hyperspectral Imaging Algorithm.Sensors (Basel, Switzerland) · 2023Article
- Classification of Skin Cancer Using Novel Hyperspectral Imaging Engineering via YOLOv5.Journal of clinical medicine · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Early detection of esophageal cancer has always been difficult, thereby reducing the overall five-year survival rate of patients. In this study, semantic segmentation was used to predict and label esophageal cancer in its early stages. U-Net was used as the basic artificial neural network along with Resnet to extract feature maps that will classify and predict the location of esophageal cancer. A total of 75 white-light images (WLI) and 90 narrow-band images (NBI) were used. These images were classified into three categories: normal, dysplasia, and squamous cell carcinoma. After labeling, the data were divided into a training set, verification set, and test set. The training set was approved by the encoder-decoder model to train the prediction model. Research results show that the average time of 111 ms is used to predict each image in the test set, and the evaluation method is calculated in pixel units. Sensitivity is measured based on the severity of the cancer. In addition, NBI has higher accuracy of 84.724% when compared with the 82.377% accuracy rate of WLI, thereby making it a suitable method to detect esophageal cancer using the algorithm developed in this study.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.