Precision in Chromosome Karyotyping: An Automated Detection System

Abstract

In this study, an intelligent system tailored explicitly 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, 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 them to diverse images containing these genetic structures. Our intelligent system presents several notable advantages. Firstly, it operates remarkably quickly, significantly reducing the time required for chromosome analysis. Secondly, it demonstrates exceptional accuracy, minimizing errors inherent in manual analysis. The implications of this system are profound, offering benefits to clinical geneticists and researchers. Medical professionals can utilize it to understand genetic conditions better, 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. We achieved outstanding precision and recall rates through meticulous fine
tuning and optimization, ensuring dependable chromosome detection and classification. This research delves into the technical intricacies of our system's creation, comprehensively evaluates its performance, and explores the profound implications for genetics.