ArticleDiagnostics (Basel, Switzerland)2022
An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification.
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 25 papers.
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Who cites it
25 citing papers in PubMed.
- Aligning deep learning and interpretable models for blood cell classification: A dual-model framework for explainability.PLOS digital health · 2026Article
- White blood cell classification using custom deep neural network and visualizing features of the images using heatmaps.Scientific reports · 2026Article
- A hybrid model for image forgery detection using deep learning with block and keypoint methods.Scientific reports · 2026Article
- Attention-Based Dual-Path Deep Learning for Blood Cell Image Classification Using ConvNeXt and Swin Transformer.Journal of imaging informatics in medicine · 2026Article
- Automatic classification of circulating blood cell clusters based on multi-channel flow cytometry imaging.Engineering applications of artificial intelligence · 2026Article
- Enhancing classification of rare white blood cells in FPM with a physics-inspired GAN.Scientific reports · 2025Article
- Automatic Classification of Circulating Blood Cell Clusters based on Multi-channel Flow Cytometry Imaging.bioRxiv : the preprint server for biology · 2025Article
- Improved leukocyte classification in bone marrow cytology using convolutional neural network with contrast enhancement.Scientific reports · 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
- Artificial intelligence-enabled microfluidic cytometer using gravity-driven slug flow for rapid CD4Microsystems & nanoengineering · 2025Article
- Optical sorting: past, present and future.Light, science & applications · 2025Review
- Advancing Laboratory Medicine Practice With Machine Learning: Swift yet Exact.Annals of laboratory medicine · 2025Review
- An explainable AI-based blood cell classification using optimized convolutional neural network.Journal of pathology informatics · 2024Article
- A hybrid approach based on multipath Swin transformer and ConvMixer for white blood cells classification.Health information science and systems · 2024Article
- Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.Journal of imaging informatics in medicine · 2024Article
- Toward Digital Periodontal Health: Recent Advances and Future Perspectives.Bioengineering (Basel, Switzerland) · 2024Review
- Classification of Cocoa Beans by Analyzing Spectral Measurements Using Machine Learning and Genetic Algorithm.Journal of imaging · 2024Article
- Article
- Blood cell image segmentation and classification: a systematic review.PeerJ. Computer science · 2024Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The evaluation of white blood cells is essential to assess the quality of the human immune system; however, the assessment of the blood smear depends on the pathologist's expertise. Most machine learning tools make a one-level classification for white blood cell classification. This work presents a two-stage hybrid multi-level scheme that efficiently classifies four cell groups: lymphocytes and monocytes (mononuclear) and segmented neutrophils and eosinophils (polymorphonuclear). At the first level, a Faster R-CNN network is applied for the identification of the region of interest of white blood cells, together with the separation of mononuclear cells from polymorphonuclear cells. Once separated, two parallel convolutional neural networks with the MobileNet structure are used to recognize the subclasses in the second level. The results obtained using Monte Carlo cross-validation show that the proposed model has a performance metric of around 98.4% (accuracy, recall, precision, and F1-score). The proposed model represents a good alternative for computer-aided diagnosis (CAD) tools for supporting the pathologist in the clinical laboratory in assessing white blood cells from blood smear images.
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