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DLBCNet: A Deep Learning Network for Classifying Blood Cells
Ziquan Zhu1, Zeyu Ren1, Siyuan Lu1
1School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK.
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.
