Abstract
Automated chromosome classification has been an important pattern recognition problem for decades. In order to im-prove the performance of automated chromosome classification, artificial intelligence and machine learning methods have been widely used in the computer-assisted chromosome detection and classification systems. This paper is focused on these algorithms, especially on artificial neural network (ANN) and wavelet transform algorithms. The principle and the realization of these algorithms are analyzed. Results of these algorithms are compared and discussed.
Highlights
Chromosomes are genetic information carriers and chromosome analysis constitutes an important procedure in clinical and cancer cytogenetics studies
In order to improve the performance of automated chromosome classification, artificial intelligence and machine learning methods have been widely used in the computer-assisted chromosome detection and classification systems
This paper is focused on these algorithms, especially on artificial neural network (ANN) and wavelet transform algorithms
Summary
Chromosomes are genetic information carriers and chromosome analysis constitutes an important procedure in clinical and cancer cytogenetics studies. Since karyotyping is a time consuming procedure, computer-based classifiers have been proposed. Most of these classifiers make use of an intuitive transformation of the chromosome image density distributions into a set of features to be used by some sort of statistical discriminator. These types of classifiers have not shown high performance results [1,2]. The principle and the realization of these algorithms are analyzed. Results of these algorithms are compared and discussed
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