计算机科学 ›› 2022, Vol. 49 ›› Issue (4): 247-253.doi: 10.11896/jsjkx.210200093
李国权1,2, 姚凯1,2, 庞宇2
LI Guo-quan1,2, YAO Kai1,2, PANG Yu2
摘要： 全血细胞计数是医学诊断中评价健康状况的重要检测手段。为解决传统血细胞计数器及其他设备对血细胞人工计数程序繁琐且耗时较长的问题,提出了一种基于卷积神经网络的血液细胞识别算法,即基于Res2Net和YOLO对象检测算法对3种类型的血液细胞进行自动识别和计数。通过将Res2Net融入YOLO模型来提取更细粒度表示的多尺度特征和增加每个网络层的感受野范围,以提升血液细胞识别模型的性能。在公开血液涂片图像数据集的训练和测试结果表明,所提方法能够自动识别和计数红细胞、白细胞和血小板,识别准确率分别达到了96.09%,93.44%,96.36%。与其他基于卷积神经网络的识别模型相比,所提方法识别准确率高且具有较强的泛化性,能显著提升血液检测的效率。
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