Development and evaluation of deep Learning architectures for human Chromosome analysis

Abstract

Chromosomes are present in cell nuclei which contain the genetic blueprint of an individual. There are 46 chromosomes (23 pairs) in every human cell out of which 22 combinations of chromosomes are autosomes and the last pair corresponds to gonosomes or sex chromosomes. The identification of many diseases can be done easily by the recognition of structural and numerical aberrations of chromosome arrangements. newlineKaryotyping refers to the arrangement of pairs of chromosomes depending on their shape, size, banding pattern and other important characteristics which will ease the analysis. The autosomes are arranged in descending order from 1 to 22, followed by gonosomes or sex chromosomes. Chromosome karyotyping is pivotal in the diagnosis of many genetic disorders and congenital disabilities. Manual karyotyping is a tedious procedure, and various techniques have been proposed in the literature to automate this process. newlineAs Artificial Intelligence (AI) algorithms exhibit their great potential in a plethora of applications, automatic chromosomal karyotyping is one of the prospective areas of applications of AI, one that is yet to be fully addressed. Deep Learning (DL) algorithms have gained tremendous attention in computer vision, specifically for chromosome image segmentation and classification. In this work, two DL architectures namely, Chromenet and Hybrid CNN-SVM, have been developed for the classification of Q-banded metaphase chromosome images. newline