A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines

2020 ◽  
Vol 67 (4) ◽  
pp. 978-986 ◽  
Author(s):  
Xiaochen Tang ◽  
Ziwei Ma ◽  
Qisong Hu ◽  
Wei Tang
Keyword(s):  
Support Vector Machines ◽  
Real Time ◽  
Classification Algorithm ◽  
Support Vector ◽  
Linear Kernel ◽  
Vector Machines ◽  
Kernel Support Vector Machines

Support Vector Machine for Recognition of White Blood Cells of Leukaemia

2011 ◽  
pp. 93-122 ◽  
Author(s):  
Stanislaw Osowski ◽  
Tomasz Markiewicz
Keyword(s):  
Support Vector Machines ◽  
Blood Cells ◽  
White Blood Cells ◽  
Myelogenous Leukaemia ◽  
Image Feature ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Vector Machines ◽  
Kernel Support Vector Machines ◽  
The Individual

This chapter presents an automatic system for white blood cell recognition in myelogenous leukaemia on the basis of the image of a bone-marrow smear. It addresses the following fundamental problems of this task: the extraction of the individual cell image of the smear, generation of different features of the cell, selection of the best features, and final recognition using an efficient classifier network based on support vector machines. The chapter proposes the complete system solving all these problems, beginning from cell extraction using the watershed algorithm; the generation of different features based on texture, geometry, morphology, and the statistical description of the intensity of the image; feature selection using linear support vector machines; and finally classification by applying Gaussian kernel support vector machines. The results of numerical experiments on the recognition of up to 17 classes of blood cells of myelogenous leukaemia have shown that the proposed system is quite accurate and may find practical application in hospitals in the diagnosis of patients suffering from leukaemia.

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