ChromosomeNet: Deep Learning-Based Automated Chromosome Detection in Metaphase Cell Images

Abstract

Goal: Chromosomes are intracellular aggregates that carry genetic information. An abnormal number or structure of chromosomes causes chromosomal disorders. Thus, chromosome screening is crucial for prenatal care; however, manual analysis of chromosomes is time consuming. With the increasing popularity of prenatal diagnosis, human labor resources are overstretched. Therefore, an automatic approach for chromosome detection and recognition is necessary. Methods: In the present study, we proposed a deep learning–based system for the automatic chromosome detection and recognition of metaphase cell images. We used a large database that included 5,000 metaphase cell images consisting of a total of 229,852 chromosomes. The proposed system was then developed and evaluated. The system, called ChromosomesNet, which combines the advantages of one-stage and two-stage models. The model uses original images as inputs without requiring preprocessing; it is thus applicable for clinical settings. To verify the clinical applicability of our system, we included 3,827 simple images and 1,173 difficult images, as identified by physicians, in our database. Results: We used COCOAPI's mAP50 evaluation method, which has average performance and a high accuracy of 99.60%. Moreover, the recall and F1 score of our proposed method were 99.9% and 99.49%, respectively. We also compared our method with five well-known object detection methods, including Faster-RCNN, YOLOv7, Retinanet, Swin transformer, and Centernet++. The results indicated that ChromosomesNet had the highest accuracy, recall, and F1 score. Unlike previous studies that have reported simple chromosome images as identification results, we obtained a 99.5% accuracy in the detection of difficult images. Conclusions: The volume of data we tested, even including difficult images, was much larger than those in the literature. The results indicated that our proposed method is sufficiently stable, robustness, and practical for clinical use. Future studies are warranted to confirm the clinical applicability of our proposed method by using data from other hospitals for cross-hospital validation.

Keywords

• Chromosome identification
• karyotype image
• convolutional neural network
• object detection