Research on sperm morphological classification based on convolutional neural network
10.19745/j.1003-8868.2024186
- VernacularTitle:基于卷积神经网络的精子形态学分类研究
- Author:
Dian YU
1
,
2
;
Feng-Ya LU
;
Zhen-Sheng ZHONG
;
Yi WANG
;
Jin-Hua ZHOU
Author Information
1. 安徽医科大学生物医学工程学院,合肥 230032
2. 安徽医科大学第一附属医院医学工程部,合肥 230032
- Keywords:
sperm morphology;
convolutional neural network;
sperm classification;
EfficientNet
- From:
Chinese Medical Equipment Journal
2024;45(10):7-13
- CountryChina
- Language:Chinese
-
Abstract:
Objective To propose a sperm classification model based on convolutional neural network to enhance the accuracy of sperm morphological classification.Methods A FT-EfficientNet model was constructed using EfficientNetB0 as the base model,which was fine-tuned by data preprocessing enhancement,transfer learning and cosine decay.Classification experi-ments were performed on the sperm public datasets SCIAN-Morpho and HuSHeM,and the datasets were segmented and vali-dated using 5-fold cross-validation.The classification results by the FT-EfficientNet model were compared with those by the cascade ensemble of support vector machines(CE-SVM)model,the adaptive patch-based dictionary learning(APDL)model,fine tuning of visual geometry group(FT-VGG)model,morphological classification of human sperm heads(MH-HSH)model and transfer learning(TL)model.Ablation experiments were performed in the SCIAN-Morpho dataset to verify the effect of different fine-tuning methods on the model.Results The FT-EfficientNet model proposed had the accuracy,precision and F1 score on the SCIAN-Morpho validation set being 64.1％,63.8％and 64.8％,respectively,which were better than CE-SVM,APDL,FT-VGG and MC-HSH models.The recall rate of the model proposed(65.2％)was slightly lower than that of MC-HSH model(68.0％).The accuracy,precision,F1 score and recall rate on the HuSHeM validation set was 95.4％,95.8％,95.4％and 96.0％,respectively,which were slightly lower than those of TL model while better than those of CE-SVM,APDL,FT-VGG and MC-HSH models.Ablation experiments showed the FT-EfficientNet model behaved the best in fine-tuning.Conclusion The sperm classification model based on convolutional neural network facilitates sperm morphology classification with high accuracy and performance.[Chinese Medical Equipment Journal,2024,45(10):7-13]
