CCGL-YOLOV5:一个跨模式跨规模的全球-本地关注YOLOV5肺瘤检测模型
Tao Zhou1, Fengzhen Liu1, Xinyu Ye1
1School of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China; Key Laboratory of Image and Graphics Intelligent Processing of State Ethnic Affairs Commission, North Minzu University, Yinchuan, 750021, China.
Computers in biology and medicine
|September 2, 2023
概括
一个新的跨模式跨规模全球本地注意力YOLOV5 (CCGL-YOLOV5) 模型改善了多模式肺瘤检测. 这种先进的AI有效地提取和融合来自PET/CT扫描的特征,以便更好地诊断.
科学领域:
- 医学图像分析 医学图像分析
- 医疗保健中的人工智能
- 在瘤学瘤学.
背景情况:
- 多式医学图像检测对于瘤诊断至关重要.
- 从肺瘤中提取特征存在挑战,因为病变的大小和形状各不相同.
研究的目的:
- 为增强多式肺瘤检测提出一种新型模型.
- 提高在医学图像中识别肺癌的准确性和效率.
主要方法:
- 开发了一个跨模式跨规模全球本地注意力YOLOV5 (CCGL-YOLOV5) 模型.
- 引入了用于特征提取和融合的跨模态融合变压器模块 (CMFTM).
- 实现全球-本地特征交互模块 (GLFIM) 进行增强的特征交互.
- 使用交叉尺度注意力融合模块 (CSAFM) 进行多尺度特征融合.
主要成果:
- 在多式肺瘤PET/CT数据集上,CCGL-YOLOV5实现了高性能指标.
- 报告的准确性 (Acc),回忆 (Rec),mAP,F1得分和FPS分别为97.83%,97.39%,96.67%,97.61%和98.59分别.
- 与其他先进型号相比,表现出卓越的性能.
结论:
- CCGL-YOLOV5模型有效地利用了多式联运特征信息.
- 这种模型对推进多式联络医学图像研究和临床肺瘤诊断具有重大意义.
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