Automated System For Chromosome Karyotyping Detection

DOI

10.2991/978-94-6463-471-6_37How to use a DOI?

Keywords

YOLO; chromosome; dataset

Abstract

In this study, an intelligent system specifically tailored for the meticulous task of identifying and categorizing chromosomes in the context of karyotyping, a critical process in genetics and medical diagnosis. To achieve, this project leveraged the capabilities of the YOLO (You Only Look Once) object detection framework, a sophisticated tool widely employed in computer vision. Our methodology involved training the system to recognize and categorize individual chromosomes by exposing it to a diverse set of images containing these genetic structures. Our intelligent system presents several notable advantages. Firstly, it operates with remarkable speed, significantly reducing the time required for chromosome analysis. Secondly, it demonstrates exceptional accuracy, thereby minimizing potential errors inherent in manual analysis. The implications of this system are profound, offering benefits to both clinical geneticists and researchers. Medical professionals can utilize it to gain a deeper understanding of genetic conditions, facilitating more precise diagnoses. Simultaneously, researchers can expedite their genetic studies, capitalizing on the efficiency of our automated system. The development process encompassed the creation of an extensive dataset comprising annotated chromosome images, serving as the foundational material for training our YOLO model. Through meticulous fine-tuning and optimization, we achieved outstanding results in terms of precision and recall rates, ensuring dependable chromosome detection and classification. This research delves into the technical intricacies of our system's creation, presents a comprehensive evaluation of its performance, and explores the profound implications for the field of genetics.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yannam Dimple Tunvey Naidu
AU  - Supriya Yaragani
AU  - Tirumala Mounika
AU  - Vamsi Vasam
AU  - Swathi Mutyala
PY  - 2024
DA  - 2024/07/30
TI  - Automated System For Chromosome Karyotyping Detection
BT  - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024)
PB  - Atlantis Press
SP  - 369
EP  - 380
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-471-6_37
DO  - 10.2991/978-94-6463-471-6_37
ID  - Naidu2024
ER  -