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An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification
Open accessAbstract
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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References 57 · Cited by 116
Research works printed but not linked: 12
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Metadata
- Authors
- Cheuque, C., Querales, M., León, R., Salas, R., & Torres, R.
- Affiliations
- Universidad Andrés Bello (CL)
- University of Valparaíso (CL)
- Centro de Investigación y Desarrollo en Ingeniería en Salud, Escuela de Ingeniería C. Biomédica, Universidad de Valparaíso, Valparaíso 2362905, Chile
- Corresponding author
- Torres, Romina
- Venue
- Diagnostics
- Publisher
- MDPI AG
- Published
- 2022-01-20
- Volume
- 12
- Issue
- 2
- Pages
- 248
- ISSN
- 2075-4418 (ISSN-L)
- PMC
- PMC8871319
- OpenAlex
- W4206685736
- Cited by
- 116
- References
- 57
- Citations by year
- 2026: 18 · 2025: 32 · 2024: 26 · 2023: 28 · 2022: 12
- FWCI
- 8.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Diagnostics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Repositorio Institucional Académico , Sistema de Bibliotecas Universidad Andrés Bello (UNAB) (Universidad Andrés Bello (UNAB)) (submittedVersion, other-oa)
- Funders
- Agencia Nacional de Investigación y Desarrollo (ICN2021- 381 004)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Imaging for Blood Diseases
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- white blood cell classification, blood smear images, lymphocytes, monocytes, neutrophils, eosinophils, Faster R-CNN, MobileNet, two-stage classification, computer-aided diagnosis
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