scholarly journals Leukocyte (White Blood Cell) Classification with a Multi-stage Support Vector Machine

2020 ◽  
pp. 216-224
Author(s):  
Hoang Truong Kien ◽  
Nguyen Hoai Phuong ◽  
Hoang Thi Luyen ◽  
Nguyen Minh Duc ◽  
Duong Trong Luong
Keyword(s):  
Support Vector Machine ◽  
Blood Cell ◽  
White Blood Cell ◽  
Support Vector ◽  
Cell Classification ◽  
Multi Stage

scholarly journals Application of the method of multiple thresholding to white blood cell classification

1988 ◽  
Vol 18 (2) ◽  
pp. 65-74 ◽  
Author(s):  
E.S. Gelsema ◽  
H.F. Bao ◽  
A.W.M. Smeulders ◽  
H.C. den Harink
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Cell Classification

scholarly journals A Neural-Network-Based Approach to White Blood Cell Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Mu-Chun Su ◽  
Chun-Yen Cheng ◽  
Pa-Chun Wang
Keyword(s):  
Blood Cell ◽  
White Blood Cell ◽  
Classification System ◽  
Blood Cells ◽  
White Blood Cells ◽  
Recognition Rate ◽  
Color Space ◽  
Segmentation Algorithm ◽  
Cell Classification ◽  
Morphological Process

This paper presents a new white blood cell classification system for the recognition of five types of white blood cells. We propose a new segmentation algorithm for the segmentation of white blood cells from smear images. The core idea of the proposed segmentation algorithm is to find a discriminating region of white blood cells on the HSI color space. Pixels with color lying in the discriminating region described by an ellipsoidal region will be regarded as the nucleus and granule of cytoplasm of a white blood cell. Then, through a further morphological process, we can segment a white blood cell from a smear image. Three kinds of features (i.e., geometrical features, color features, and LDP-based texture features) are extracted from the segmented cell. These features are fed into three different kinds of neural networks to recognize the types of the white blood cells. To test the effectiveness of the proposed white blood cell classification system, a total of 450 white blood cells images were used. The highest overall correct recognition rate could reach 99.11% correct. Simulation results showed that the proposed white blood cell classification system was very competitive to some existing systems.

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