[1]邓竹琴,俞永伟.改进型深度学习模型在乳腺肿瘤良恶性鉴别中的应用[J].中国医学物理学杂志,2020,37(11):1469-1473.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.023]
　DENG Zhuqin,YU Yongwei.Application of improved deep learning model in differential diagnosis of benign and malignant breast tumors[J].Chinese Journal of Medical Physics,2020,37(11):1469-1473.[doi:DOI:10.3969/j.issn.1005-202X.2020.11.023]
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改进型深度学习模型在乳腺肿瘤良恶性鉴别中的应用()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
37
期数:
2020年第11期
页码:
1469-1473
栏目:
医学人工智能
出版日期:
2020-12-02

文章信息/Info

Title:
Application of improved deep learning model in differential diagnosis of benign and malignant breast tumors
文章编号:
1005-202X（2020）11-1469-05
作者:
邓竹琴1; 俞永伟2
1.中国人民解放军联勤保障部队第901医院妇产科， 安徽 合肥 230031； 2.安徽省合肥市长荣医院普外科， 安徽 合肥 230001
Author(s):
DENG Zhuqin1;  YU Yongwei2
1. Department of Obstetrics and Gynecology, The Peoples Liberation Army Joint Service Support Unit No.901 Hospital, Hefei 230031, China 2. Department of General Surgery, Changrong Hospital, Hefei 230001, China
关键词:
卷积神经网络; 乳腺癌细胞识别; 图像识别
Keywords:
Keywords: Convolutional neural network breast tumor cell recognition image recognition
分类号:
R318；R377.9
DOI:
DOI:10.3969/j.issn.1005-202X.2020.11.023
文献标志码:
A
摘要:
目的：解决传统方法在临床中对病理图像检测不足以及人工判断导致的错误判断等问题。方法：使用乳腺肿瘤细胞数据集，首先对数据集进行数据增强，增强后数据集为原来的2倍，将增强后数据集输入到本文提出的模型中进行训练。结果：经过100次迭代，训练集的准确率为97.44%，在测试集中准确率为96.4%，召回率为95.5%，与同类型文献相比都有明显提高。结论：本文章提出的改进型卷积神经网络具有收敛快，泛化好等特点。可以对乳腺肿瘤细胞的良恶性进行有效的辨识分类。
Abstract:
Abstract: Objective To improve the defects and deficiency of traditional methods in clinical pathological image detection and to solve the problem of misjudgment made by human. Methods The data sets of breast tumor cells are from clinical date. The data sets were enhanced two fold at first and put into the proposed model for training. Results After 100 times of iterations, the accuracy of validation set arrives 97.44%, the test set 96.4%, and the recall rate 95.5%.They are obviously improved compared with the same type of literature. Conclusion The improved convolutional neural network proposed in this paper has advantages of rapid convergence and excellent generalization ability. It can identify and classify the benign and malignant breast tumor cells effectively.

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备注/Memo

备注/Memo:
【收稿日期】2020-07-15 【作者简介】邓竹琴，妇产科主治医师，E-mail: 951340480@qq.com 【通信作者】俞永伟，普外科主治医师，E-mail: 278718482@qq.com
更新日期/Last Update: 2020-12-02