ArticleFrontiers in oncology2023
Using deep DenseNet with cyclical learning rate to classify leukocytes for leukemia identification.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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12 citing papers in PubMed, 27 citations in OpenAlex.
- An interpretable deep learning framework for multiclass bone marrow cytomorphology classification using EfficientNet and post hoc visualization techniques.Journal of pathology informatics · 2026Article
- XHIC-Net: An Explainable Hybrid Involution-Convolution Network for Blood Smear Cell Morphology Classification.Bioengineering (Basel, Switzerland) · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective.Scientific reports · 2026Article
- A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.BMC medical informatics and decision making · 2025Article
- A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.Scientific reports · 2025Article
- Discrete wavelet integrated convolutional residual network for bearing fault diagnosis under noise and variable operating conditions.Scientific reports · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Advanced leukocyte classification using attention mechanisms and dual channel U-Net architecture.Scientific reports · 2025Article
- Deep neural network modeling for brain tumor classification using magnetic resonance spectroscopic imaging.PLOS digital health · 2025Article
- A Chronological Overview of Using Deep Learning for Leukemia Detection: A Scoping Review.Cureus · 2024Article
- A hybrid deep learning scheme for MRI-based preliminary multiclassification diagnosis of primary brain tumors.Frontiers in oncology · 2024Article
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Authors and funding
7 authors at 5 institutions in 3 countries.
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No grant is acknowledged in the PubMed record.
Abstract
Background: The examination, counting, and classification of white blood cells (WBCs), also known as leukocytes, are essential processes in the diagnosis of many disorders, including leukemia, a kind of blood cancer characterized by the uncontrolled proliferation of carcinogenic leukocytes in the marrow of the bone. Blood smears can be chemically or microscopically studied to better understand hematological diseases and blood disorders. Detecting, identifying, and categorizing the many blood cell types are essential for disease diagnosis and therapy planning. A theoretical and practical issue. However, methods based on deep learning (DL) have greatly helped blood cell classification. Materials and Methods: Images of blood cells in a microscopic smear were collected from GitHub, a public source that uses the MIT license. An end-to-end computer-aided diagnosis (CAD) system for leukocytes has been created and implemented as part of this study. The introduced system comprises image preprocessing and enhancement, image segmentation, feature extraction and selection, and WBC classification. By combining the DenseNet-161 and the cyclical learning rate (CLR), we contribute an approach that speeds up hyperparameter optimization. We also offer the one-cycle technique to rapidly optimize all hyperparameters of DL models to boost training performance. Results: The dataset has been split into two sets: approximately 80% of the data (9,966 images) for the training set and 20% (2,487 images) for the validation set. The validation set has 623, 620, 620, and 624 eosinophil, lymphocyte, monocyte, and neutrophil images, whereas the training set has 2,497, 2,483, 2,487, and 2,499, respectively. The suggested method has 100% accuracy on the training set of images and 99.8% accuracy on the testing set. Conclusion: Using a combination of the recently developed pretrained convolutional neural network (CNN), DenseNet, and the one fit cycle policy, this study describes a technique of training for the classification of WBCs for leukemia detection. The proposed method is more accurate compared to the state of the art.
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