ArticleMedical & biological engineering & computing2020
Combining DC-GAN with ResNet for blood cell image classification.
Article in Medical & biological engineering & computing, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 133 citations in OpenAlex.
- XHIC-Net: An Explainable Hybrid Involution-Convolution Network for Blood Smear Cell Morphology Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Spectral-aware CNN with learnable biorthogonal units and depthwise convolutions for multi-class blood cell classification.MethodsX · 2025Article
- Enhancing classification of rare white blood cells in FPM with a physics-inspired GAN.Scientific reports · 2025Article
- Research on concept generation design of automobile seats based on human-machine co-creation.Scientific reports · 2025Article
- A medical image classification method based on self-regularized adversarial learning.Medical physics · 2024Article
- Data augmentation based on the WGAN-GP with data block to enhance the prediction of genes associated with RNA methylation pathways.Scientific reports · 2024Article
- An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2024Article
- Automated diagnosis of adenoid hypertrophy with lateral cephalogram in children based on multi-scale local attention.Scientific reports · 2024Article
- Effects of Crude Extract ofAntioxidants (Basel, Switzerland) · 2024Article
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- Automated bone marrow cell classification through dual attention gates dense neural networks.Journal of cancer research and clinical oncology · 2023Article
- An Intelligent Attention-Based Transfer Learning Model for Accurate Differentiation of Bone Marrow Stains to Diagnose Hematological Disorder.Life (Basel, Switzerland) · 2023Article
- Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.Frontiers in oncology · 2023Article
- Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study.Frontiers in neurology · 2023Article
- Automated Bone Marrow Cell Classification for Haematological Disease Diagnosis Using Siamese Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2022Article
- BCNet: A Deep Learning Computer-Aided Diagnosis Framework for Human Peripheral Blood Cell Identification.Diagnostics (Basel, Switzerland) · 2022Article
- Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning.Biomedical optics express · 2022Article
- Synthesis of Microscopic Cell Images Obtained from Bone Marrow Aspirate Smears through Generative Adversarial Networks.Biology · 2022Article
- A Method for Expanding the Training Set of White Blood Cell Images.Journal of healthcare engineering · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In medicine, white blood cells (WBCs) play an important role in the human immune system. The different types of WBC abnormalities are related to different diseases so that the total number and classification of WBCs are critical for clinical diagnosis and therapy. However, the traditional method of white blood cell classification is to segment the cells, extract features, and then classify them. Such method depends on the good segmentation, and the accuracy is not high. Moreover, the insufficient data or unbalanced samples can cause the low classification accuracy of model by using deep learning in medical diagnosis. To solve these problems, this paper proposes a new blood cell image classification framework which is based on a deep convolutional generative adversarial network (DC-GAN) and a residual neural network (ResNet). In particular, we introduce a new loss function which is improved the discriminative power of the deeply learned features. The experiments show that our model has a good performance on the classification of WBC images, and the accuracy reaches 91.7%. Graphical Abstract Overview of the proposed method, we use the deep convolution generative adversarial networks (DC-GAN) to generate new samples that are used as supplementary input to a ResNet, the transfer learning method is used to initialize the parameters of the network, the output of the DC-GAN and the parameters are applied the final classification network. In particular, we introduced a modified loss function for classification to increase inter-class variations and decrease intra-class differences.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.