Improved YOLOv8-seg for laryngeal structure recognition in medical images

The SlimMSDA-YOLO model is lightweight and efficient, making it ideal for real-time laryngeal structure recognition during intubation. Comparative experiments with other lightweight segmentation networks highlight the effectiveness and superiority of the proposed approach.

Authors

Haipo Cui (ORCID: https://orcid.org/0000-0001-7619-9560)

Institutions

University of Shanghai for Science and Technology (CN)

Publication Details

Journal: American Journal of Translational Research
Published: 2025-01-01
DOI: https://doi.org/10.62347/bihi3707
Citations: 1
Primary Topic: Speech Recognition and Synthesis
Type: article
Field-Weighted Citation Impact: 2.48

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article

Improved YOLOv8-seg for laryngeal structure recognition in medical images

Haipo Cui

Jan 1, 2025

1 citations

American Journal of Translational Research

Speech Recognition and Synthesis

2.48

article

Improved YOLOv8-seg for laryngeal structure recognition in medical images

Haipo Cui

January 1, 2025article en

1 citations

Abstract

American Journal of Translational ResearchVol. 17(5)

University of Shanghai for Science and Technology (CN)

Openalex Percentile: Top 10%

Speech Recognition and Synthesis

2.48

DOI Open Access PDF OpenAlex

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Improved YOLOv8-seg for laryngeal structure recognition in medical images — Haipo Cui · American Journal of Translational Research (2025) | TGRS Research Map | TGRS