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Human Chromosome Classification Using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

TitleHuman Chromosome Classification Using Competitive Neural Network Teams (CNNT) and Nearest Neighbor
Publication TypeConference Proceedings
Year of Publication2014
AuthorsGagula-Palalic, S, Can, M
Conference NameIEEE-EMBS International Conferences on Biomedical and Health Informatics
Date Published06/2014
Place PublishedValencia, Spain
Abstract

This paper presents a novel approach to human chromosome classification. Human cell contains 22 pairs of
autosomes and a pair of sex chromosomes. In this research, 22 types of autosomes represent 22 classes to be distinguished.
New method of classification is based on the special organized committee of 462 simple perceptrons, called Competitive
Neural Network Teams (CNNTs). Each perceptron is trained to differentiate two classes (i.e. two types of chromosome),
hence there are 22 x 21 learning machines. Moreover, dummy perceptrons are set to zero for the chromosomes from the same
class. The final outcome of the testing data is a 22x22 decision matrix, containing outcomes of each machine. With the special
interpretation of these decisions, higher correct classification rate is achieved, reaching over 95%. The method can be
further improved when testing is performed on a cell-by-cell basis by using CNNT complemented by Nearest Neighbor
technique. The classification is applied to the Copenhagen chromosome data set and Sarajevo chromosome data set.