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Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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Optimized Yolov8 feature fusion algorithm for dental disease detection.

Qimeng Wang1, Xingfei Zhu2, Zhaofei Sun1

  • 1Jiangnan University, Wuxi, Jiangsu, 214122, China; The Jiangsu Key Laboratory of Advanced Food Manufacturing Equipment and Technology, Wuxi, Jiangsu, 214122, China.

Computers in Biology and Medicine
|February 9, 2025
PubMed
Summary

This study introduces an improved YEM-SAFN model for enhanced dental disease detection from panoramic films. The model significantly boosts accuracy in identifying dental conditions, overcoming challenges like image distortion.

Keywords:
Attention mechanismObject detectionOral panoramic slicesSmall objectsWeighted fusionYOLOv8s

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Area of Science:

  • Dentistry
  • Computer Vision
  • Medical Imaging

Background:

  • Panoramic dental films suffer from magnification distortion and low contrast, hindering accurate disease detection.
  • Existing object detection algorithms perform suboptimally in identifying dental conditions due to these image quality issues.

Purpose of the Study:

  • To develop an improved YEM-SAFN model for enhanced recognition of dental conditions in oral panoramic films.
  • To address challenges of small targets, multi-scale variations, and tissue overlap in dental imaging.

Main Methods:

  • Incorporated a novel small-target network structure and multi-scale detection heads.
  • Integrated the HCSA attention mechanism for focused feature extraction in disease-specific regions.
  • Implemented a redesigned weighted fusion module to improve multi-scale feature utilization.

Main Results:

  • The improved YEM-SAFN algorithm achieved a mean Average Precision (mAP) of 86.7%, a 3.2% increase over the original YOLOv8s.
  • Demonstrated superior performance compared to other mainstream object detection algorithms.
  • Effectively addressed multi-scale targets and feature extraction challenges.

Conclusions:

  • The enhanced YEM-SAFN model offers an efficient and accurate solution for dental condition identification and diagnosis.
  • This approach improves the reliability of automated detection systems for dental diseases.