Classification of white blood cells using weighted optimized deformable convolutional neural networks
Open Access
- 1 January 2021
- journal article
- research article
- Published by Taylor & Francis in Artificial Cells, Nanomedicine, and Biotechnology
- Vol. 49 (1), 147-155
- https://doi.org/10.1080/21691401.2021.1879823
Abstract
Machine learning (ML) algorithms have been widely used in the classification of white blood cells (WBCs). However, the performance of ML algorithms still needs to be addressed for being short of go...Keywords
This publication has 40 references indexed in Scilit:
- A new acute leukaemia-automated classification systemComputer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 2016
- The emerging role of immune checkpoint inhibition in malignant lymphomaHaematologica, 2016
- Automatic detection and classification of leukocytes using convolutional neural networksMedical & Biological Engineering & Computing, 2016
- A spectral and morphologic method for white blood cell classificationOptics & Laser Technology, 2016
- Maintenance therapy of childhood acute lymphoblastic leukemia revisited—Should drug doses be adjusted by white blood cell, neutrophil, or lymphocyte counts?Pediatric Blood & Cancer, 2016
- Cancer statistics in China, 2015CA: A Cancer Journal for Clinicians, 2016
- Image segmentation and classification of white blood cells with the extreme learning machine and the fast relevance vector machineArtificial Cells, Nanomedicine, and Biotechnology, 2015
- Cancer incidence and mortality worldwide: Sources, methods and major patterns in GLOBOCAN 2012International Journal of Cancer, 2014
- Automatic segmentation, counting, size determination and classification of white blood cellsMeasurement, 2014
- A CGA-MRF Hybrid Method for Iris Texture Analysis and ModelingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2014