Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China.
Key Laboratory of Advanced Manufacturing Technology, Ministry of Education, Guizhou University, Guiyang, Guizhou, 550025, China; Information Engineering Institute, Chongqing Vocational and Technical University of Mechatronics, Chongqing, 402760, China.
Comput Biol Med. 2023 Mar;154:106606. doi: 10.1016/j.compbiomed.2023.106606. Epub 2023 Jan 23.
White blood cell (WBC) detection in microscopic images is indispensable in medical diagnostics; however, this work, based on manual checking, is time-consuming, labor-intensive, and easily results in errors. Using object detectors for WBCs with deep convolutional neural networks can be regarded as a feasible solution. In this paper, to improve the examination precision and efficiency, a one-stage and lightweight CNN detector with an attention mechanism for detecting microscopic WBC images, and a white blood cell detection vision system are proposed. The method integrates different optimizing strategies to strengthen the feature extraction capability through the combination of an improved residual convolution module, hybrid spatial pyramid pooling module, improved coordinate attention mechanism, efficient intersection over union (EIOU) loss and Mish activation function. Extensive ablation and contrast experiments on the latest public Raabin-WBC dataset verify the effectiveness and robustness of the proposed detector for achieving a better overall detection performance. It is also more efficient than other existing studies for blood cell detection on two additional classic public BCCD and LISC datasets. The novel detection approach is significant and flexible for medical technicians to use for blood cell microscopic examination in clinical practice.
白细胞（WBC）检测在医学诊断中是必不可少的；然而，这项基于人工检查的工作既耗时又费力，并且容易出错。使用带有深度卷积神经网络的对象检测器检测 WBC 可以被视为一种可行的解决方案。在本文中，为了提高检查精度和效率，提出了一种用于检测显微镜下 WBC 图像的具有注意机制的单阶段轻量级 CNN 检测器和一个白细胞检测视觉系统。该方法通过结合改进的残差卷积模块、混合空间金字塔池化模块、改进的坐标注意力机制、高效交并比（EIOU）损失和 Mish 激活函数，集成了不同的优化策略，以通过组合来增强特征提取能力。在最新的公共 Raabin-WBC 数据集上进行的广泛的消融和对比实验验证了所提出的检测器在实现更好的整体检测性能方面的有效性和鲁棒性。与其他现有的用于在两个附加的经典公共 BCCD 和 LISC 数据集上进行血细胞检测的研究相比，它的效率也更高。这种新的检测方法对于医学技术人员在临床实践中进行血细胞显微镜检查具有重要意义和灵活性。
