ArticleBMC bioinformatics2022
Accurate classification of white blood cells by coupling pre-trained ResNet and DenseNet with SCAM mechanism.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- An interpretable deep learning framework for multiclass bone marrow cytomorphology classification using EfficientNet and post hoc visualization techniques.Journal of pathology informatics · 2026Article
- Deep Learning-Based Detection Model for Leukemia Cells in Peripheral Blood Smears Using YOLOv11-Large.Current oncology (Toronto, Ont.) · 2026Article
- Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.PLOS digital health · 2026Article
- An expert-level vision-language model for multitask diagnostic morphology in clinical laboratories.NPJ digital medicine · 2026Article
- Deep Learning Identifies Abnormal Promyelocytes in Peripheral Blood Based on Morphological Analysis.Diagnostics (Basel, Switzerland) · 2026Article
- Test-Time Augmentation for Cross-Domain Leukocyte Classification via OOD Filtering and Self-Ensembling.Journal of imaging · 2025Article
- CTCNet: a fine-grained classification network for fluorescence images of circulating tumor cells.Medical & biological engineering & computing · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- A two stage blood cell detection and classification algorithm based on improved YOLOv7 and EfficientNetv2.Scientific reports · 2025Article
- Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.Annals of hematology · 2025Article
- Deep generative classification of blood cell morphology.Nature machine intelligence · 2025Article
- Machine learning-directed electrical impedance tomography to predict metabolically vulnerable plaques.Bioengineering & translational medicine · 2024Article
- Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.Frontiers in oncology · 2023Article
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7 authors.
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Abstract
backgroundVia counting the different kinds of white blood cells (WBCs), a good quantitative description of a person's health status is obtained, thus forming the critical aspects for the early treatment of several diseases. Thereby, correct classification of WBCs is crucial. Unfortunately, the manual microscopic evaluation is complicated, time-consuming, and subjective, so its statistical reliability becomes limited. Hence, the automatic and accurate identification of WBCs is of great benefit. However, the similarity between WBC samples and the imbalance and insufficiency of samples in the field of medical computer vision bring challenges to intelligent and accurate classification of WBCs. To tackle these challenges, this study proposes a deep learning framework by coupling the pre-trained ResNet and DenseNet with SCAM (spatial and channel attention module) for accurately classifying WBCs.
resultsIn the proposed network, ResNet and DenseNet enables information reusage and new information exploration, respectively, which are both important and compatible for learning good representations. Meanwhile, the SCAM module sequentially infers attention maps from two separate dimensions of space and channel to emphasize important information or suppress unnecessary information, further enhancing the representation power of our model for WBCs to overcome the limitation of sample similarity. Moreover, the data augmentation and transfer learning techniques are used to handle the data of imbalance and insufficiency. In addition, the mixup approach is adopted for modeling the vicinity relation across training samples of different categories to increase the generalizability of the model. By comparing with five representative networks on our developed LDWBC dataset and the publicly available LISC, BCCD, and Raabin WBC datasets, our model achieves the best overall performance. We also implement the occlusion testing by the gradient-weighted class activation mapping (Grad-CAM) algorithm to improve the interpretability of our model.
conclusionThe proposed method has great potential for application in intelligent and accurate classification of WBCs.
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