ArticleScientific reports2024
White blood cells classification using multi-fold pre-processing and optimized CNN model.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- XHIC-Net: An Explainable Hybrid Involution-Convolution Network for Blood Smear Cell Morphology Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Enrichment of Hyaluronic Acid Binding Tumor Cells by Modulation of Selective Adhesion on Microgel Surfaces.Macromolecular rapid communications · 2026Article
- Artificial Intelligence and Data Science Methods for Automatic Detection of White Blood Cells in Images.Journal of imaging informatics in medicine · 2026Review
- Efficient blood cell classification from microscopic smear images using U-Net segmentation and a lightweight CNN.Scientific reports · 2025Article
- Enhanced Efficiency of the Microsomal Prostaglandin EMolecular pharmaceutics · 2025Article
- Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.Scientific reports · 2025Article
- Aqueous Vernonia amygdalina leaf extract in drinking water mitigates aflatoxin B1 toxicity in broilers: effects on performance, biomarker analysis, and liver histology.Mycotoxin research · 2025Article
- Integration of nanobiosensors into organ-on-chip systems for monitoring viral infections.Nano convergence · 2024Review
- An Innovative Hybrid Model for Automatic Detection of White Blood Cells in Clinical Laboratories.Diagnostics (Basel, Switzerland) · 2024Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
White blood cells (WBCs) play a vital role in immune responses against infections and foreign agents. Different WBC types exist, and anomalies within them can indicate diseases like leukemia. Previous research suffers from limited accuracy and inflated performance due to the usage of less important features. Moreover, these studies often focus on fewer WBC types, exaggerating accuracy. This study addresses the crucial task of classifying WBC types using microscopic images. This study introduces a novel approach using extensive pre-processing with data augmentation techniques to produce a more significant feature set to achieve more promising results. The study conducts experiments employing both conventional deep learning and transfer learning models, comparing performance with state-of-the-art machine and deep learning models. Results reveal that a pre-processed feature set and convolutional neural network classifier achieves a significantly better accuracy of 0.99. The proposed method demonstrates superior accuracy and computational efficiency compared to existing state-of-the-art works.
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What OpenQuestion holds
Registered trials
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.