DLBCNet：一种用于血细胞分类的深度学习网络。
DLBCNet: A Deep Learning Network for Classifying Blood Cells.
作者信息
Zhu Ziquan, Ren Zeyu, Lu Siyuan, Wang Shuihua, Zhang Yudong
机构信息
School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.
Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
出版信息
Big Data Cogn Comput. 2023 Apr 14;7(2):75. doi: 10.3390/bdcc7020075.
BACKGROUND
Blood is responsible for delivering nutrients to various organs, which store important health information about the human body. Therefore, the diagnosis of blood can indirectly help doctors judge a person's physical state. Recently, researchers have applied deep learning (DL) to the automatic analysis of blood cells. However, there are still some deficiencies in these models.
METHODS
To cope with these issues, we propose a novel network for the multi-classification of blood cells, which is called DLBCNet. A new specifical model for blood cells (BCGAN) is designed to generate synthetic images. The pre-trained ResNet50 is implemented as the backbone model, which serves as the feature extractor. The extracted features are fed to the proposed ETRN to improve the multi-classification performance of blood cells.
RESULTS
The average accuracy, average sensitivity, average precision, average specificity, and average f1-score of the proposed model are 95.05%, 93.25%, 97.75%, 93.72%, and 95.38%, accordingly.
CONCLUSIONS
The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.
背景
血液负责将营养物质输送到各个器官，这些器官存储着有关人体的重要健康信息。因此，血液诊断可以间接帮助医生判断一个人的身体状况。最近，研究人员已将深度学习（DL）应用于血细胞的自动分析。然而，这些模型仍存在一些不足之处。
方法
为了解决这些问题，我们提出了一种用于血细胞多分类的新型网络，称为DLBCNet。设计了一种新的血细胞特定模型（BCGAN）来生成合成图像。预训练的ResNet50被用作骨干模型，作为特征提取器。提取的特征被输入到所提出的ETRN中，以提高血细胞的多分类性能。
结果
所提出模型的平均准确率、平均灵敏度、平均精确率、平均特异性和平均F1分数分别为95.05%、93.25%、97.75%、93.72%和95.38%。
结论
在所报道的分类结果中，所提出模型的性能超过了其他现有最先进的方法。
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