An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification
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Fecha
2022-02
Profesor/a Guía
Facultad/escuela
Idioma
en
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ISSN de la revista
Título del volumen
Editor
MDPI
Nombre de Curso
Licencia CC
Attribution 4.0 International (CC BY 4.0)
Licencia CC
https://www.mdpi.com/openaccess
Resumen
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.
Notas
Indexación: Scopus.
Palabras clave
Deep learning, Multi-level classification, Multisource datasets, White blood cells classification
Citación
Diagnostics, Volume 12, Issue 2, February 2022, Article number 248
DOI
10.3390/diagnostics12020248