A Novel Automated Chromosome Analyzer Software Bundle for Karyotyping and Birth Defect Analysis

Abstract

Karyotyping is a procedure to diagnose birth defects using chromosome pair. During the Karyotyping chromosomes arranged based on the length and each chromosome will be paired based on various parameters such as, chromosome length, banding pattern and Centromere position. Many methods are proposed to identify the above parameters to improve the Karyotyping accuracy. Since, it’s a challenging task for researchers to improve the accuracy of Karyotyping compared with clinical assays. In this paper, a novel computer geometry method is proposed for chromosome Karyotyping using inbuilt deep learning models with algorithms for chromosome segmentation, overlapped separation, banding pattern analysis and classification of chromosomes. Chromosome classification is carried out using various deep learning models and automated Karyotyping is carried out without manual intervention and model improved the Karyotyping accuracy over the existing methods. In this paper novel computer geometry method is proposed to automate the chromosome analysis in Matlab environment. The developed software provides the accuracy of 99.68% in classification and karyotyping.

Keywords

• Deep learning
• chromosome
• karyotyping
• banding pattern