The classification performance of MMV-Net model for benign and malignant masses on X-ray mammography using deep learning
10.11904/j.issn.1002-3070.2024.03.006
- VernacularTitle:深度学习MMV-Net模型对乳腺X线良性和恶性肿块的分类效能
- Author:
Jiahao LI
1
;
Jiahe BAI
;
Jie LAN
;
Haixia LI
;
Yan ZHANG
;
Jianghong SUN
Author Information
1. 哈尔滨医科大学附属肿瘤医院(哈尔滨 150081)
- Keywords:
Deep learning;
MMV-Net model;
Mammography;
Tumor;
Classification
- From:
Practical Oncology Journal
2024;38(3):179-183
- CountryChina
- Language:Chinese
-
Abstract:
Objective The MMV-Net,a deep learning framework based on mammogram multiple views,was constructed to evaluate the classification performance of the model for benign and malignant masses.Methods A retrospective analysis was conduc-ted on a dataset of 1 585 breast X-ray images from Harbin Medical University Cancer Hospital from 2018 to 2020,including 806 be-nign cases and 779 malignant cases.The dataset was divided into the training set(n=1268)and the test set(n=317)according to an 8∶2 ratios,and the training set was stratified according to the 5-fold cross validation.The integrated DDSM dataset and INBreast dataset were used as external test sets(n=1645)to evaluate the model performance.Each case in the input layer contained 4 views.The MMV-Net model was constructed by removing the last two layers of the ResNet22 network structure and adding an average poo-ling layer as the feature extraction layer,as well as fully connection layer and softmax activation function as the decision layers.Bayes-ian hyperparameter optimization was used.The performance of MMV-Net,MFA Net,and ensemble inception V4 models in AUC val-ues,accuracy,precision,recall and F1 scores were compared.Results The AUC values of MMV-Net model for distinguishing benign and malignant masses on the test set were 0.913,0.882 for MFA-Net,and 0.865 for inception V4.The accuracy and precision evalu-ation metrics of the MMV-Net model were also higher than the other two models.Conclusion The deep learning MMV-Net model based on multiple views of mammogram is helpful for the classification of benign and malignant breast masses.
