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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00064173%3A_____%2F24%3A43928416" target="_blank" >RIV/00064173:_____/24:43928416 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11120/24:43928416

Výsledek na webu

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

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

30206 - Otorhinolaryngology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název periodika

Procedia Computer Science

ISSN

1877-0509

e-ISSN

1877-0509

Svazek periodika

246

Číslo periodika v rámci svazku

C

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

9

Strana od-do

4998-5006

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85213341748