Automated bone marrow cell classification through dual attention gates dense neural networks.
Attention gates
Bone marrow cell classification
Computer-aided diagnosis
Leukemia
Neural network
Journal
Journal of cancer research and clinical oncology
ISSN: 1432-1335
Titre abrégé: J Cancer Res Clin Oncol
Pays: Germany
ID NLM: 7902060
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
07
06
2023
accepted:
31
08
2023
medline:
20
11
2023
pubmed:
23
9
2023
entrez:
23
9
2023
Statut:
ppublish
Résumé
The morphology of bone marrow cells is essential in identifying malignant hematological disorders. The automatic classification model of bone marrow cell morphology based on convolutional neural networks shows considerable promise in terms of diagnostic efficiency and accuracy. However, due to the lack of acceptable accuracy in bone marrow cell classification algorithms, automatic classification of bone marrow cells is now infrequently used in clinical facilities. To address the issue of precision, in this paper, we propose a Dual Attention Gates DenseNet (DAGDNet) to construct a novel efficient, and high-precision bone marrow cell classification model for enhancing the classification model's performance even further. DAGDNet is constructed by embedding a novel dual attention gates (DAGs) mechanism in the architecture of DenseNet. DAGs are used to filter and highlight the position-related features in DenseNet to improve the precision and recall of neural network-based cell classifiers. We have constructed a dataset of bone marrow cell morphology from the First Affiliated Hospital of Chongqing Medical University, which mainly consists of leukemia samples, to train and test our proposed DAGDNet together with the bone marrow cell classification dataset. When evaluated on a multi-center dataset, experimental results show that our proposed DAGDNet outperforms image classification models such as DenseNet and ResNeXt in bone marrow cell classification performance. The mean precision of DAGDNet on the Munich Leukemia Laboratory dataset is 88.1%, achieving state-of-the-art performance while still maintaining high efficiency. Our data demonstrate that the DAGDNet can improve the efficacy of automatic bone marrow cell classification and can be exploited as an assisting diagnosis tool in clinical applications. Moreover, the DAGDNet is also an efficient model that can swiftly inspect a large number of bone marrow cells and offers the benefit of reducing the probability of an incorrect diagnosis.
Identifiants
pubmed: 37740765
doi: 10.1007/s00432-023-05384-9
pii: 10.1007/s00432-023-05384-9
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
16971-16981Subventions
Organisme : Chongqing Postdoctoral Science Fund
ID : CSTB2022NSCQ-BHX0682
Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
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