Evaluation for application of automatic detection of sperm head morphology based on deep learning
10.13602/j.cnki.jcls.2023.10.05
- VernacularTitle:基于深度学习的精子头形态自动检测应用评估
- Author:
Yi WU
1
;
Jie SHI
;
Yili YANG
;
Jinxing LV
Author Information
1. 苏州市独墅湖医院生殖医学中心,江苏苏州 215123
- Keywords:
convolutional neural network;
sperm head morphology;
deep learning
- From:
Chinese Journal of Clinical Laboratory Science
2023;41(10):736-739
- CountryChina
- Language:Chinese
-
Abstract:
Objective To design an automated detection protocol for morphological detection of sperm head based on deep learning and evaluate its efficiency,accuracy and reliability.Methods Fourteen pictures for each of 1 000 samples were analyzed by using the pre-trained YOLO target detection model and VGG16 classification model.At least 200 sperm were detected for each sample.Equal amount of samples were analyzed by manual microscope examination,and the efficiency,accuracy and correlation between the two methods were compared.Results The morphology of sperm heads which were manually classified but untrained was detected by pre-trained classification model,and the prediction accuracy reached to 95.5％.The detection time for clinical each sample was only 10 seconds,and its accuracy and efficiency were higher than those of manual microscope examination.The percentages of the sperms with normal morphology were significantly positively correlated(r=0.84,P＜0.01)in the detections of both the methods.Conclusion The detec-tion protocol proposed in this study can greatly improve work efficiency,and its reliability and accuracy exceed those of manual micro-scope examination.
