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Related Experiment Video

Updated: Jun 23, 2025

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

A machine learning approach for vocal fold segmentation and disorder classification based on ensemble method.

S M Nuruzzaman Nobel¹, S M Masfequier Rahman Swapno¹, Md Rajibul Islam²

¹Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh.

Scientific Reports

|June 23, 2024

Summary

This summary is machine-generated.

This study integrates vocal fold (VF) disease classification and segmentation using ensemble machine learning. The system achieves high accuracy, offering a powerful tool for precise VF disorder diagnostics and improved patient care.

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Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

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Published on: December 2, 2011

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Last Updated: Jun 23, 2025

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Investigating the Three-dimensional Flow Separation Induced by a Model Vocal Fold Polyp

Published on: February 3, 2014

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications

Author Spotlight: Advancements in the Fabrication of Synthetic Vocal Fold Models for Phonetic and Robotic Applications

Published on: January 5, 2024

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Synthetic, Multi-Layer, Self-Oscillating Vocal Fold Model Fabrication

Published on: December 2, 2011

Area of Science:

Medical Imaging
Machine Learning in Healthcare
Otolaryngology

Background:

Accurate classification and segmentation of vocal fold (VF) diseases are critical for effective diagnosis and treatment.
Integrating these two tasks into a single system presents a significant challenge in healthcare diagnostics.
Existing methods may lack the precision required for comprehensive VF disorder assessment.

Purpose of the Study:

To develop an integrated system for simultaneous vocal fold (VF) disease classification and segmentation.
To evaluate the efficacy of ensemble machine learning models for this combined diagnostic task.
To enhance diagnostic accuracy and provide clinicians with advanced tools for VF disorder management.

Main Methods:

Utilized ensemble EfficientNetV2L-LGBM for vocal fold (VF) disease classification.
Employed ensemble UNet-BiGRU for vocal fold (VF) segmentation.
Implemented and refined segmentation techniques to improve data partitioning accuracy.

Main Results:

The classification model achieved a test accuracy of 97.88%.
The segmentation model attained a test accuracy of 91.47% with an Intersection over Union (IOU) of 87.46%.
Optimized segmentation methods further improved accuracy to 91.99%.

Conclusions:

The integrated system demonstrates high performance in both vocal fold (VF) disease classification and segmentation.
This approach represents a significant advancement in diagnostic tools for VF disorders.
The study highlights the potential of machine learning to revolutionize VF disease identification and management, offering hope for improved patient outcomes.