DLBCNet: A Deep Learning Network for Classifying Blood Cells

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 a...

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Bibliographic Details
Published in:Big data and cognitive computing 2023-04, Vol.7 (2), p.75-75
Main Authors: Zhu, Ziquan, Ren, Zeyu, Lu, Siyuan, Wang, Shuihua, Zhang, Yudong
Format: Article
Language:English
Subjects:
Accuracy
Blood cells
Blood tests
Classification
Deep learning
Disease
generative adversarial networks
Leukocytes
Malaria
Neural networks
Nutrients
randomized neural network
ResNet50
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Summary: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. 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. 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. The performance of the proposed model surpasses other state-of-the-art methods in reported classification results.
ISSN:2504-2289
2504-2289
DOI:10.3390/bdcc7020075