Comparative Analysis of YOLO-based Models for Vocal Cord Segmentation in Laryngoscopic Images

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60461373%3A22340%2F24%3A43930241" target="_blank" >RIV/60461373:22340/24:43930241 - isvavai.cz</a>

Alternative codes found

RIV/68407700:21220/24:00383682

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1877050924024980" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1877050924024980</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2024.09.457" target="_blank" >10.1016/j.procs.2024.09.457</a>

Result language

angličtina

Original language name

Comparative Analysis of YOLO-based Models for Vocal Cord Segmentation in Laryngoscopic Images

Original language description

This study presents a comparative analysis of segmentation models based on the YOLO (You Only Look Once) architecture for the task of vocal cord detection in laryngoscopic images. The yolov5, yolov8, and yolov9 architectures were evaluated using images obtained from laryngoscopic videos recorded during standard examinations at ORL clinics. The primary objective was to assess the efficiency of different model sizes and architectures in accurately identifying the position of vocal cords within the images. Our findings reveal that all evaluated architectures demonstrate proficiency in vocal cord detection, with comparable results across the models. However, there is a discernible difference in mean Average Precision (mAP) metrics (at IoU thresholds ranging from 0.5 to 0.95). Notably, yolov8 exhibits the highest mAP scores, followed by yolov5 and yolov9, indicating superior performance in identifying vocal cord regions. This comparative analysis provides valuable insights into the effectiveness of YOLO-based segmentation models for vocal cord detection, highlighting the importance of model size and architecture selection in medical image analysis applications.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Procedia Computer Science

ISBN

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ISSN

1877-0509

e-ISSN

1877-0509

Number of pages

9

Pages from-to

4998-5006

Publisher name

Elsevier B.V.

Place of publication

Amsterdam

Event location

Seville

Event date

Sep 11, 2024

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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