ArticlePloS one2022
WBC-AMNet: Automatic classification of WBC images using deep feature fusion network based on focalized attention mechanism.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 30 citations in OpenAlex.
- Article
- Advancing WBC Classification: A Hybrid ConvNextV2-Swin Transformer Framework with R3GAN Data Balancing and CLAHE Preprocessing.Journal of imaging informatics in medicine · 2026Article
- Next-generation morphology, a novel multilayer morphometric digital analysis, reveals both the basic topology and new trends of myelodysplasia of peripheral blood specimens.British journal of haematology · 2025Article
- A Hybrid VGG16-ViT Approach With Image Processing Techniques for Improved White Blood Cell Classification and Disease Diagnosis: A Retrospective Study.Health science reports · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Discriminating Spectral-Spatial Feature Extraction for Hyperspectral Image Classification: A Review.Sensors (Basel, Switzerland) · 2024Review
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
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Abstract
The recognition and classification of White Blood Cell (WBC) play a remarkable role in blood-related diseases (i.e., leukemia, infections) diagnosis. For the highly similar morphology of different WBC subtypes, it is too confused to classify the WBC effectively and accurately for visual observation of blood cell smears. This paper proposes a Deep Convolutional Neural Network (DCNN) with feature fusion strategies, named WBC-AMNet, for automatically classifying WBC subtypes based on focalized attention mechanism. To obtain more localized attention of CNN, the fusion features of the first and the last convolutional layer are extracted by focalized attention mechanism combining Squeeze-and-Excitation (SE) and Gather-Excite (GE) modules. The new method performs successfully in classifying monocytes, neutrophils, lymphocytes, and eosinophils on the complex background with an overall accuracy of 95.66%, better than that of general CNNs. The multi-classification accuracy of WBC-AMNet with the background segmentation is over 98% in all cases. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) is employed to visualize the attention heatmaps of different feature maps.
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