ArticleSensors (Basel, Switzerland)2021
Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 35 citations in OpenAlex.
- Application of artificial intelligence in laryngeal lesions: a systematic review and meta-analysis.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2025Pooled it
- An interpretable multi-instance learning method for accurate differentiation of malignant and benign laryngeal lesions in laryngoscopy.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Uncertainty-Aware Prediction Across Endoscopic Domains: Laryngeal Narrow-Band and Gastrointestinal Imaging.Biomedicines · 2026Article
- A transformer-based multi-modal fusion framework for laryngeal lesion classification using contact endoscopy.Frontiers in surgery · 2026Article
- A Panoramic View of Narrow Band Imaging in the Treatment of Head and Neck CancerCurrent medical imaging · 2026Review
- Targeted Screening Strategies for Head and Neck Cancer: A Global Review of Evidence, Technologies, and Cost-Effectiveness.Diagnostics (Basel, Switzerland) · 2025Review
- Enhancing clinical diagnosis of laryngeal cancer through fusion-based transfer learning with Osprey Optimisation Algorithm using histology images.Frontiers in oncology · 2025Article
- Improving laryngeal cancer detection using chaotic metaheuristics integration with squeeze-and-excitation resnet model.Health information science and systems · 2024Article
- Improving Laryngoscopy Image Analysis Through Integration of Global Information and Local Features in VoFoCD Dataset.Journal of imaging informatics in medicine · 2024Article
- Enhanced WGAN Model for Diagnosing Laryngeal Carcinoma.Cancers · 2024Article
- Artificial Intelligence in Head and Neck Surgery.Otolaryngologic clinics of North America · 2024Review
- Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment.Scientific data · 2023Article
- Instance segmentation of upper aerodigestive tract cancer: site-specific outcomes.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2023Article
- An Improvised Deep-Learning-Based Mask R-CNN Model for Laryngeal Cancer Detection Using CT Images.Sensors (Basel, Switzerland) · 2022Article
- Hierarchical dynamic convolutional neural network for laryngeal disease classification.Scientific reports · 2022Article
- Artificial intelligence in clinical endoscopy: Insights in the field of videomics.Frontiers in surgery · 2022Review
Corrections and comments
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Authors and funding
9 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.
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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.