ArticleDiagnostics (Basel, Switzerland)2022
Classification of White Blood Cells: A Comprehensive Study Using Transfer Learning Based on Convolutional Neural Networks.
Article in Diagnostics (Basel, Switzerland), 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.
- Advancing WBC Classification: A Hybrid ConvNextV2-Swin Transformer Framework with R3GAN Data Balancing and CLAHE Preprocessing.Journal of imaging informatics in medicine · 2026Article
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Possibilities and limitations of digital microscopy of blood smear of the modern hematological analyser Sysmex XN-3100 in leukocyte differentiation.Journal of medical biochemistry · 2025Article
- Gastroprotective, Antioxidant, Anti-Inflammatory, and Toxicological Evaluation of Stem Bark Extracts ofPharmaceuticals (Basel, Switzerland) · 2025Article
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- White blood cells classification using multi-fold pre-processing and optimized CNN model.Scientific reports · 2024Article
- Parking Time Violation Tracking Using YOLOv8 and Tracking Algorithms.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
3 authors.
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Abstract
White blood cells (WBCs) in the human immune system defend against infection and protect the body from external hazardous objects. They are comprised of neutrophils, eosinophils, basophils, monocytes, and lymphocytes, whereby each accounts for a distinct percentage and performs specific functions. Traditionally, the clinical laboratory procedure for quantifying the specific types of white blood cells is an integral part of a complete blood count (CBC) test, which aids in monitoring the health of people. With the advancements in deep learning, blood film images can be classified in less time and with high accuracy using various algorithms. This paper exploits a number of state-of-the-art deep learning models and their variations based on CNN architecture. A comparative study on model performance based on accuracy, F1-score, recall, precision, number of parameters, and time was conducted, and DenseNet161 was found to demonstrate a superior performance among its counterparts. In addition, advanced optimization techniques such as normalization, mixed-up augmentation, and label smoothing were also employed on DenseNet to further refine its performance.
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Registered trials
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