使用YOLOv8进行面部博托克斯注射点检测,并使用CBAM和BiFPN进行增强:多视角深度学习方法
Sedanur Savas1, Elif Baykal Kablan2, Murat Ekinci3
1Computer Engineering, Karadeniz Technical University, Trabzon, 61080, Turkey. sedanursavas2@gmail.com.
Journal of imaging informatics in medicine
|February 12, 2026
概括
这项研究引入了一种使用YOLOv8与CBAM和BiFPN的AI系统,用于精确检测Botox注射点,提高化品皮肤病学的准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 化品皮肤科 化品皮肤科
背景情况:
- 肉毒注射在化品皮肤病学中常见,用于减少纹,但在技术上具有挑战性.
- 在确定精确的博托克斯注射点方面存在很高的变化,这会影响程序的一致性和结果.
研究的目的:
- 开发一个增强的YOLOv8物体检测框架,用于准确和自动识别Botox注射点.
- 提高面部图像中Botox注射部位定位的精度和一致性.
主要方法:
- 通过整合卷积块注意模块 (CBAM) 和双向特征金字塔网络 (BiFPN) 开发了一个增强的YOLOv8对象检测框架.
- 该系统在一个新的高分辨率数据集上进行了评估,其中包括四个临床相关的面部图像子集.
主要成果:
- 拟议的框架显示了与基线YOLOv8n架构相比的一致的性能改进.
- 相对平均平均精度 (mAP@0.5) 的增长范围从1.3%到4.2%,在局部化小纹方面显著增强.
- 集成CBAM和BiFPN模块显著提高了注射点检测的准确性.
结论:
- 开发的AI系统为自动和精确的博托克斯注射点检测提供了一个有希望的解决方案.
- 这项技术有可能在化品皮肤病学中作为人工智能辅助的临床决策支持,提高程序的准确性和患者的结果.
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