Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers

Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers In current consequence of haematology, blood cancer i.e. acute lymphoblastic leukemia is very frequently founded in medical practice, which is characterized by over activation and functional abnormality of bone marrow. The abnormality is identified through physical examination with a screening of blood smears. However, this method is error prone and labor intensive task for haematologist. Hence, haematologist needs a specific computer aided diagnostic system (CAD) that can deal with these limitations of prior systems and capable of discriminating immature leukemic cells from mature healthy cells. Thus, this work addresses the problem of segmenting a microscopic blood image into different regions, and then further analyzes those regions for localization of the immature lymphoblast cell. Further, it investigates the use of different geometrical, chromatic and statistical textures features for nucleus as well as cytoplasm and pattern recognition techniques for sub typing immature acute lymphoblasts as per FAB (French– American – British) classification. This can facilitate haematologist for acquiring essential information about prognosis and for an appropriate cure for leukemia. The exhaustive experiments have been conducted on 260 microscopic blood images (i.e. 130 normal and 130 cancerous cells) taken from ALL-IDB database. The proposed techniques consisting of the segmentation module used for segmenting the nucleus and cytoplasm of each leukocyte cell, feature extraction module, feature dimensionality reduction module that uses principal component analysis (PCA) to mapped the higher feature space to lower feature space and classification module that employs the standard classifiers, like support vector machines, smooth support vector machines, k-nearest neighbour, probabilistic neural network and adaptive neuro fuzzy inference system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

Classification of acute lymphoblastic leukaemia using hybrid hierarchical classifiers

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4478-3
Publisher site
See Article on Publisher Site

Abstract

In current consequence of haematology, blood cancer i.e. acute lymphoblastic leukemia is very frequently founded in medical practice, which is characterized by over activation and functional abnormality of bone marrow. The abnormality is identified through physical examination with a screening of blood smears. However, this method is error prone and labor intensive task for haematologist. Hence, haematologist needs a specific computer aided diagnostic system (CAD) that can deal with these limitations of prior systems and capable of discriminating immature leukemic cells from mature healthy cells. Thus, this work addresses the problem of segmenting a microscopic blood image into different regions, and then further analyzes those regions for localization of the immature lymphoblast cell. Further, it investigates the use of different geometrical, chromatic and statistical textures features for nucleus as well as cytoplasm and pattern recognition techniques for sub typing immature acute lymphoblasts as per FAB (French– American – British) classification. This can facilitate haematologist for acquiring essential information about prognosis and for an appropriate cure for leukemia. The exhaustive experiments have been conducted on 260 microscopic blood images (i.e. 130 normal and 130 cancerous cells) taken from ALL-IDB database. The proposed techniques consisting of the segmentation module used for segmenting the nucleus and cytoplasm of each leukocyte cell, feature extraction module, feature dimensionality reduction module that uses principal component analysis (PCA) to mapped the higher feature space to lower feature space and classification module that employs the standard classifiers, like support vector machines, smooth support vector machines, k-nearest neighbour, probabilistic neural network and adaptive neuro fuzzy inference system.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Mar 11, 2017

References

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