Vol.12, No.3, August 2023.                                                                                                                                                                               ISSN: 2217-8309

                                                                                                                                                                                                                        eISSN: 2217-8333

TEM Journal

TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS

Association for Information Communication Technology Education and Science

Prediction of Centromere Location in Human Chromosome Using Convolutional Neural Networks

Ajdin Vatreš, Naris Pojskić, Edin Kadrić

© 2023 Ajdin Vatreš, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)

Citation Information: TEM Journal. Volume 12, Issue 3, Pages 1242-1251, ISSN 2217-8309, DOI: 10.18421/TEM123-02, August 2023.

Received: 25 April 2023.

Revised:   13 June 2023.
Accepted: 24 July 2023.
Published: 28 August 2023.

Abstract:

Accurate determination of chromosome centromere location is of high importance in cytogenetics, particularly in karyotyping, chromosome classification and determination of exposure to genotoxic environmental effects. This study investigates the ability of CNN to accurately predict the human chromosome centromere location and the effect centering chromosomes in images, by predicted centromere location, has on classification accuracy. Dataset, used to train and test CNN models, contained 8283 annotated individual chromosome images. Prior to performing centromere detection, followed by chromosome classification, the individual chromosome images are preprocessed using sequence of filtering algorithms. The CNN model achieved an average error of 0.5586 and 0.4543 in predicting x and y coordinates of centromere location, respectively. The achieved classification accuracy of randomly oriented and centered chromosomes in images, is 71.10 and 96.73%, respectively. Achieved increase in chromosome classification accuracy of 25.63% highlights importance of chromosome centromere detection, importance of positional variation removal, and high performance of CNN in prediction of centromere location and chromosome classification.

Keywords –Convolutional network, chromosome centromere detection, chromosome classification, image filtering.

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