ArticleBiomedical engineering online2023
Convolutional neural network-based vocal cord tumor classification technique for home-based self-prescreening purpose.
Article in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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Who cites it
8 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- A Systematic Review of the Clinical Impact of Implementing Artificial Intelligence in Upper Aerodigestive Tract Endoscopy.Head & neck · 2025Pooled it
- Exploring Real-Time Tracking of Vocal Fold Polyps in Video-Stroboscopy Using Deep Learning.The Laryngoscope · 2026Article
- Videomics and artificial intelligence in endoscopic diagnosis of laryngeal lesions: mapping current evidence through a scoping review.Acta otorhinolaryngologica Italica : organo ufficiale della Societa italiana di otorinolaringologia e chirurgia cervico-facciale · 2026Review
- CKAD-Net: class-guided key-feature aggregation and adaptive decision network for vocal cord lesion prediction.Scientific reports · 2026Article
- Use of Artificial Intelligence for Diagnosing Oral Mucosa Conditions: A Review.Diagnostics (Basel, Switzerland) · 2026Review
- Classifying Phonotrauma Severity from Vocal Fold Images with Soft Ordinal Regression.Proceedings of machine learning research · 2026Article
- Vocal Fold Disorders Classification and Optimization of a Custom Video Laryngoscopy Dataset Through Structural Similarity Index and a Deep Learning-Based Approach.Journal of clinical medicine · 2025Article
- A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.Scientific reports · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundIn this study, we proposed a deep learning technique that can simultaneously detect suspicious positions of benign vocal cord tumors in laparoscopic images and classify the types of tumors into cysts, granulomas, leukoplakia, nodules and polyps. This technique is useful for simplified home-based self-prescreening purposes to detect the generation of tumors around the vocal cord early in the benign stage.
resultsWe implemented four convolutional neural network (CNN) models (two Mask R-CNNs, Yolo V4, and a single-shot detector) that were trained, validated and tested using 2183 laryngoscopic images. The experimental results demonstrated that among the four applied models, Yolo V4 showed the highest F1-score for all tumor types (0.7664, cyst; 0.9875, granuloma; 0.8214, leukoplakia; 0.8119, nodule; and 0.8271, polyp). The model with the lowest false-negative rate was different for each tumor type (Yolo V4 for cysts/granulomas and Mask R-CNN for leukoplakia/nodules/polyps). In addition, the embedded-operated Yolo V4 model showed an approximately equivalent F1-score (0.8529) to that of the computer-operated Yolo-4 model (0.8683).
conclusionsBased on these results, we conclude that the proposed deep-learning-based home screening techniques have the potential to aid in the early detection of tumors around the vocal cord and can improve the long-term survival of patients with vocal cord tumors.
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