White Blood Cell Automatic Counting System Based on Support Vector Machine
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
Leukocyte segmentation and SVM classification in blood smear images
Machine Graphics & Vision International Journal
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Computer Methods and Programs in Biomedicine
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IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Leukocyte detection using nucleus contour propagation
Miar'06 Proceedings of the Third international conference on Medical Imaging and Augmented Reality
A computer assisted method for leukocyte nucleus segmentation and recognition in blood smear images
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ICCVG'12 Proceedings of the 2012 international conference on Computer Vision and Graphics
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A clinical decision support system known as Leuko has been developed for leukemia diagnosis using a Naive Bayes classifier. The system is able to recognize six types of white blood cells (WBC), including a malignancy. This paper investigates the use of Support Vector Machines (SVMs) classifiers to recognize WBC for future leukemia diagnosis. Since SVMs are originally designed for the solution of two class problems, several strategies for their extension to this multiclass task are investigated and compared. The experimental results evidence the potential of SVMs to leukemia diagnosis and indicate that an hierarchical tree-based multiclass strategy can be better suited to a future update of the Leuko system.