Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics · 2024
[Application of convolutional neural networks for the classification of metaphase chromosomes].
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Xu Lingling, Zhou Ying, Zhang Lichao, Wang Zhenyu, Mao Qianqian, Song Ning, Li Haibo
Аннотация
OBJECTIVE: To train a deep convolutional neural networks (CNN) using a labeled data set to classify the metaphase chromosomes and test its accuracy for chromosomal identification.
METHODS: Three thousand and three hundred individuals undergoing surveillance for chromosomal disorders at the Laboratory for Comprehensive Prevention and Treatment of Birth Defects, Ningbo Maternal and Child Health Care Hospital from January 2013 to July 2019 were enrolled. A total of 3 300×46 chromosome images were included, of which 70% were used as the training set and 30% were used as the test set for the deep CNN. The accuracy of chromosome counting and "cutting + recognition + arrangement + automatic analysis" of the model were respectively evaluated. Another 80 images were collected to record the time and accuracy of chromosome classification by geneticists and the model, respectively, so as to assess the practical value of the model.
RESULTS: The CNN model was used to count the chromosomes with an accuracy of 61.81%, and the "cutting + recognition + arrangement + automatic analysis" accuracy of the model was 96.16%. Compared with manual operation, the classification time of the CNN model has been greatly reduced, and its karyotyping accuracy was only 3.58% lower than that of geneticists.
CONCLUSION: The CNN model has a high performance for chromosome classification and can significantly reduce the work load involved with the segmentation and classification and improve the efficiency of chromosomal karyotyping, thereby has a broad application prospect.
METHODS: Three thousand and three hundred individuals undergoing surveillance for chromosomal disorders at the Laboratory for Comprehensive Prevention and Treatment of Birth Defects, Ningbo Maternal and Child Health Care Hospital from January 2013 to July 2019 were enrolled. A total of 3 300×46 chromosome images were included, of which 70% were used as the training set and 30% were used as the test set for the deep CNN. The accuracy of chromosome counting and "cutting + recognition + arrangement + automatic analysis" of the model were respectively evaluated. Another 80 images were collected to record the time and accuracy of chromosome classification by geneticists and the model, respectively, so as to assess the practical value of the model.
RESULTS: The CNN model was used to count the chromosomes with an accuracy of 61.81%, and the "cutting + recognition + arrangement + automatic analysis" accuracy of the model was 96.16%. Compared with manual operation, the classification time of the CNN model has been greatly reduced, and its karyotyping accuracy was only 3.58% lower than that of geneticists.
CONCLUSION: The CNN model has a high performance for chromosome classification and can significantly reduce the work load involved with the segmentation and classification and improve the efficiency of chromosomal karyotyping, thereby has a broad application prospect.