基于改进的深度学习模型分类乳腺癌分子亚型  

Improved Deep Learning Models to Classify Breast Cancer Molecular Subtypes

作　　者：段美华 焦雄 DUAN Meihua;JIAO Xiong(College of Biomedical Engineering,Taiyuan University of Technology,Jinzhong 030600,China)

机构地区：[1]太原理工大学生物医学工程学院,山西晋中030600

出　　处：《中北大学学报（自然科学版）》2024年第4期428-438,共11页Journal of North University of China(Natural Science Edition)

基　　金：山西省自然科学基金资助项目(202203021221063)。

摘　　要：由于乳腺癌的高度异质性,不同分子亚型的乳腺癌在治疗和诊断方面都有着很大的差异,因此提高乳腺癌亚型诊断的准确性,对于进一步降低乳腺癌的误诊率,避免乳腺癌的过度治疗具有重要的意义。基于深度学习算法对TCIA数据库中的MRI乳腺癌医学图像进行了乳腺癌Luminal A和非Luminal A的分子亚型的分类研究。为了对比深度学习算法在分类乳腺癌分子亚型方面的优越性,使用TCGA数据库基因表达数据进行了乳腺癌分子亚型分类研究;在相同乳腺癌MRI图像数据库下,基于传统机器学习算法进行了乳腺癌分子亚型分类研究。在利用深度学习算法处理MRI图像时,对比了多种深度学习模型;微调VGG16网络,冻结卷积层;添加densenet网络模块改进了VGG16网络模型,即得到VGG16+densenet(4)模型。改进的VGG16网络模型的准确率达到0.96, AUC达到0.97;基因表达数据分类准确率为0.73, AUC为0.79;传统机器学习的分类准确率最高达到0.80, AUC达到0.87。实验结果表明,提出的VGG16+densenet(4)模型提高了乳腺癌分子亚型的准确率,具有更好的分类效果。Due to the high heterogeneity of breast cancer,different molecular subtypes of breast cancer have great differences in the treatment and diagnosis.Therefore,it is of important research significance to improve the accuracy of the diagnosis of breast cancer subtypes,thus to further reduce the misdiagnosis rate of breast cancer and avoid overtreatment of breast cancer.The molecular subtypes of Luminal A and non-Luminal A of breast cancer were classified based on deep learning algorithm for MRI breast cancer medical images in TCIA database.In order to compare the superiority of deep learning algorithm in classification of breast cancer molecular subtypes,gene expression data of TCGA database was used to classify breast cancer molecular subtypes.In the same breast cancer MRI image database,the classification of breast cancer molecular subtypes was studied based on traditional machine learning algorithm.Comparative studies between a variety of deep learning models were conducted in the processing of MRI imageswith deep learning algorithm. Fine-tuning VGG16 network and frozen convolution layer were implemented.An improved VGG16 network model was proposed, and a densenet network module was added,namely VGG16+densenet (4) model. The accuracy of the improved VGG16 network model was 0. 96,and the AUC was 0. 97. The classification accuracy of gene expression data was 0. 73, and the AUC was0. 79. The classification accuracy and AUC of traditional machine learning reached 0. 80 and 0. 87 respectively.The experimental results show that the proposed VGG16+densenet( 4) model improves the accuracyof the molecular subtypes of breast cancer and has better classification effect.

关 键 词：深度学习 乳腺癌分子亚型 传统机器学习 数据增强 基因表达数据 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]