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基于变压器骨干网络的改进的Yolov5s用于检测和分类支气管支气管洗细胞

Puzhen Wu^1,2, Han Weng², Wenting Luo³

¹The Faculty of Architecture, Civil and Transportation Engineering, Beijing University of Technology, Beijing 100124, China.

Computational and structural biotechnology journal

|May 30, 2023

概括

这项研究引入了一种改进的AI模型,用于准确检测和分类支气管洗液 (BALF) 中的肺细胞. 与现有方法相比,增强的Yolov5s网络实现了更高的准确性和效率.

科学领域:

医疗成像医学成像
计算生物学计算生物学
人工智能的人工智能

背景情况:

支气管洗液 (BALF) 提供肺组织信息,补充了传统的肺活检病理学.
准确检测和分类BALF细胞 (巨细胞,淋巴细胞,中性粒细胞,乙酸细胞) 是由于细胞相似性和处理变异的挑战.
现有的方法在精确识别BALF细胞方面遇到了困难.

研究的目的:

提出一个改进的深度学习模型来检测和分类四种关键的BALF细胞类型.
通过使用先进的神经网络架构,提高BALF细胞分析的准确性和效率.

主要方法:

一个改进的Yolov5s网络,采用Swin变压器V2骨干,用于全球信息获取.
将C3Ghost模块集成到子网络中,以优化功能融合和减少参数.
用于加快和准确的界限框回归的联合损失 (EIoU_Loss) 上的嵌入交叉点.

主要成果:

拟议的模型实现了81.29%的平均平均精度 (mAP) 和80.47%的回忆率.
与原来的Yolov5s (mAP +3.3%,召回 +3.67%) 相比,显示出显著的改进.
在mAP中表现优于Yolov7和Yolov8 (分别高出0.02%和2.36%) 具有更好的每秒 (FPS).

结论:

增强的Yolov5s模型有效地提高了BALF细胞检测和分类准确度.
与最先进的方法相比,该模型在效率和准确性之间提供了更好的平衡.
这种方法为肺细胞学自动化分析提供了有希望的进步.

关键词:

支气管支气管洗细胞的细胞.细胞检测检测细胞检测卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.变压器变压器变压器

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