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Convolutional neural network-based vocal cord tumor classification technique for home-based self-prescreening

Gun Ho Kim1,2, Young Jun Hwang3, Hongje Lee4

  • 1Medical Research Institute, Pusan National University, Yangsan, Korea.

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A new deep learning technique enables early detection of benign vocal cord tumors through home self-screening. This method accurately identifies and classifies various tumor types, aiding in timely diagnosis and improved patient outcomes.

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Convolutional neural networkDeep learningOtolaryngologyVocal cord tumor

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Vocal cord tumors require early detection for better prognosis.
  • Current screening methods may not be easily accessible for home-based self-assessment.
  • Benign vocal cord tumors include cysts, granulomas, leukoplakia, nodules, and polyps.

Purpose of the Study:

  • To develop a deep learning technique for simultaneous detection and classification of benign vocal cord tumors.
  • To enable simplified home-based self-prescreening for early tumor detection.
  • To improve early diagnosis of vocal cord tumors in their benign stage.

Main Methods:

  • Implementation of four convolutional neural network (CNN) models: two Mask R-CNNs, Yolo V4, and a single-shot detector.
  • Training, validation, and testing of models using 2183 laryngoscopic images.
  • Evaluation of model performance based on F1-scores and false-negative rates for different tumor types.

Main Results:

  • Yolo V4 demonstrated the highest F1-scores across all tested benign vocal cord tumor types.
  • Specific models showed the lowest false-negative rates for different tumor classifications (Yolo V4 for cysts/granulomas, Mask R-CNN for others).
  • An embedded Yolo V4 model achieved performance comparable to its computer-operated counterpart.

Conclusions:

  • Deep learning-based home screening techniques show potential for early vocal cord tumor detection.
  • The proposed methods can aid in improving long-term survival rates for patients with vocal cord tumors.
  • AI-powered tools can facilitate accessible and effective vocal cord health monitoring.