Human chromosome classification using Competitive Neural Network Teams (CNNT) and Nearest Neighbor

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 × 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 22×22 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.