A classification of chronic leukaemia using new extension of k-means clustering and EFMM based on digital microscopic blood images

被引：0

作者：

Kalaiselvi C. ^{[1
]}

Asokan R. ^{[2
]}

机构：

[1] Department of Electronics and Communication Engineering, Kongu Engineering College, Erode, Tamil Nadu

[2] Department of Electronics and Communication Engineering, Kongu Nadu College of Technology, Trichy, Tamil Nadu

来源：

Kalaiselvi, C. (kalaiselvi7345@gmail.com) | 1600年 / Inderscience Enterprises Ltd., 29, route de Pre-Bois, Case Postale 856, CH-1215 Geneva 15, CH-1215, Switzerland卷 / 23期

关键词：

EFMM neural network; Hausdorff dimension; New extension κ-means clustering algorithm; Pre-processing;

D O I：

10.1504/IJBET.2017.082664

中图分类号：

学科分类号：

摘要：

Leukaemia is a cancer of the white blood cells. The type of white blood cell affected in either lymphoid or myeloid. And leukaemia is defined in two ways, such as acute leukaemia (AL) and chronic leukaemia (CL). These kinds of leukaemia start when typical blood cells change and grow wildly. This paper describes in the following steps to classify the chronic leukaemia automatically and more accurately. First, pre-processing the colour scale of digital microscope blood image, then segment the image by new extension of k-means clustering algorithm, and Hausdorff dimension (HD) is utilised for feature extraction, finally the classification is done by utilising Enhanced Fuzzy Min Max (EFMM) neural network. The proposed method obtained 99.95% accuracy for Lymphocytic and Myelogenous cells. © 2017 Inderscience Enterprises Ltd.

引用

页码：232 / 241

页数：9

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