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%2F60461373%3A22340%2F24%3A43930241" target="_blank" >RIV/60461373:22340/24:43930241 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/68407700:21220/24:00383682

Výsledek na webu

<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>

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

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

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

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Procedia Computer Science

ISBN

—

ISSN

1877-0509

e-ISSN

1877-0509

Počet stran výsledku

9

Strana od-do

4998-5006

Název nakladatele

Elsevier B.V.

Místo vydání

Amsterdam

Místo konání akce

Seville

Datum konání akce

11. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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