Karyotyping of comparative genomic hybridization human metaphases by using support vector machines

Kou, Zhenzhen; Liang, Jing; Zhang, Xuegong

doi:10.1002/cyto.10027

Cited by 13 publications

(2 citation statements)

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“…Nowadays, several efforts have been done to create automatic systems for dealing with computer-based karyotyping [2,[6][7][8][9][10][11][12][13]. Several studies implement techniques from machine learning such as Support Vector Machines [7,8,[14][15][16], Nearest Neighbor Algorithms [17,18], Wavelets [19], Bayesian techniques [20,21] and mainly, Artificial Neural Networks [19,[22][23][24][25]. Nevertheless, the automatic computer image-based karyotyping is an open research topic.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

2020

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This work proposes the evaluation of a set of algorithms of machine learning and the selection of the most appropriate one for the classification of segmented chromosomes images acquired using the Giemsa staining technique (G-banding). The evaluation and selection of the best classification algorithms was carried out over a dataset of 119 Q-banding chromosomes images, and the obtained results were then applied to a dataset of 24 G-band chromosomes images, manually classified by an expert of the Laboratory of Cytogenetic of the Children’s Hospital of Tamaulipas. The results of evaluation of 51 classifiers yielded that the best classification accuracy for the selected features was obtained by a backpropagation neural network. One of the main contributions of this study is the proposal of a two-stage classification scheme based on the best classifier found by the initial evaluation. In stage 1, chromosome images are classified into three major groups. In stage 2, the output of phase 1 is used as the input of a multiclass classifier. Using this scheme, 82% of the IGB bank samples and 88% of the samples of a bank of images obtained with a Q-band available in the literature consisting of 119 chromosome studies were successfully classified. The proposed work is a part of an desktop application that allows cytogeneticist to automatically generate cytogenetic reports.

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Section: Introductionmentioning

confidence: 99%

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

2020

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“…Four-color CGH (14) is concerned with quality control, not karyotyping. To our knowledge, the only attempt to utilize color information for karyotyping CGH images was made by Kou et al (15). However, we use a different approach in this study.…”

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confidence: 99%

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest‐neighbor algorithm

Liang

2002

Cytometry

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BackgroundComparative genomic hybridization (CGH) is a relatively new molecular cytogenetic method that detects chromosomal imbalances. Automatic karyotyping is an important step in CGH analysis because the precise position of the chromosome abnormality must be located and manual karyotyping is tedious and time‐consuming. In the past, computer‐aided karyotyping was done by using the 4′,6‐diamidino‐2‐phenylindole, dihydrochloride (DAPI)‐inverse images, which required complex image enhancement procedures.MethodsAn innovative method, kernel nearest‐neighbor (K‐NN) algorithm, is proposed to accomplish automatic karyotyping. The algorithm is an application of the “kernel approach,” which offers an alternative solution to linear learning machines by mapping data into a high dimensional feature space. By implicitly calculating Euclidean or Mahalanobis distance in a high dimensional image feature space, two kinds of K‐NN algorithms are obtained. New feature extraction methods concerning multicolor information in CGH images are used for the first time.ResultsExperiment results show that the feature extraction method of using multicolor information in CGH images improves greatly the classification success rate. A high success rate of about 91.5% has been achieved, which shows that the K‐NN classifier efficiently accomplishes automatic chromosome classification from relatively few samples.ConclusionsThe feature extraction method proposed here and K‐NN classifiers offer a promising computerized intelligent system for automatic karyotyping of CGH human chromosomes. Cytometry 48:202–208, 2002. © 2002 Wiley‐Liss, Inc.

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Chromosome segmentation and classification: an updated review

Somasundaram,

Madian,

Goh

et al. 2024

Knowl Inf Syst

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Karyotyping of comparative genomic hybridization human metaphases by using support vector machines

Cited by 13 publications

References 21 publications

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

Machine Learning Classifiers Evaluation for Automatic Karyogram Generation from G-Banded Metaphase Images

Karyotyping of comparative genomic hybridization human metaphases using kernel nearest‐neighbor algorithm

Chromosome segmentation and classification: an updated review

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