An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification

65Citations
Citations of this article
77Readers
Mendeley users who have this article in their library.

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.

Cite

CITATION STYLE

APA

Cheuque, C., Querales, M., León, R., Salas, R., & Torres, R. (2022). An Efficient Multi-Level Convolutional Neural Network Approach for White Blood Cells Classification. Diagnostics, 12(2). https://doi.org/10.3390/diagnostics12020248

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free