基于DenseNet201的乳腺癌病理图像的预测研究

韩杨; 苗壮; 孙悦; 郭金兴; 陈广新; 高铭泽

doi:10.3969/j.issn.2096-6091.2024.01.002

您当前的位置：

首页 >

文章列表页 >

基于DenseNet201的乳腺癌病理图像的预测研究

研究论文 | 更新时间：2024-12-16

- 基于DenseNet201的乳腺癌病理图像的预测研究
- Predictive Study of Breast Cancer Pathological Images Based on DenseNet201
- 新一代信息技术 2024年7卷第1期页码：6-11
- 作者机构：
  
  1.牡丹江医学院医学影像学院，黑龙江牡丹江 157011
  2.牡丹江医学院基础医学院，黑龙江牡丹江 157011
  3.牡丹江医学院附属红旗医院，黑龙江牡丹江 157011
- 作者简介：
  
  韩杨（1990—），女，黑龙江人，硕士学位，讲师，研究方向：医学人工智能。
  苗壮（2000—），男，黑龙江人，硕士研究生在读，研究方向：基础医学。
  孙悦（1992—），女，黑龙江人，初级职称，研究方向：人工智能。
  郭金兴（1984—），女，黑龙江人，中级职称，研究方向：人工智能。
  陈广新（1978—），男，黑龙江人，中级职称，研究方向：医学图像处理。E-mail： 44642581@qq.com
  高铭泽
- 基金信息：
  
  黑龙江省属高校科研基本业务费科研项目(2023-KYYWF-0940)
- DOI：10.3969/j.issn.2096-6091.2024.01.002
  中图分类号： R195
- 纸质出版日期：2024-01-25，
- 稿件说明：
移动端阅览
韩杨, 苗壮, 孙悦, 等. 基于DenseNet201的乳腺癌病理图像的预测研究[J]. 新一代信息技术, 2024, 7(1): 06-11

HAN Yang, MIAO Zhuang, SUN Yue, et al. Predictive Study of Breast Cancer Pathological Images Based on DenseNet201[J]. New Generation of Information Technology, 2024, 7(1): 06-11
韩杨, 苗壮, 孙悦, 等. 基于DenseNet201的乳腺癌病理图像的预测研究[J]. 新一代信息技术, 2024, 7(1): 06-11 DOI： 10.3969/j.issn.2096-6091.2024.01.002.

HAN Yang, MIAO Zhuang, SUN Yue, et al. Predictive Study of Breast Cancer Pathological Images Based on DenseNet201[J]. New Generation of Information Technology, 2024, 7(1): 06-11 DOI： 10.3969/j.issn.2096-6091.2024.01.002.

摘要

本文研究利用DenseNet201网络构建乳腺病理图像分类预测模型。本研究的数据集包括162个乳腺癌标本，并使用DenseNet121进行实验对比。对比结果显示DenseNet201相较于DenseNet121在乳腺癌检测上的表现更为出色，通过20个训练epochs，整体准确性达82%，未患有浸润性导管癌类别精确度90%、召回率84%，

分数为0.87。患有浸润性导管癌类别精确度66%、召回率77%，

分数为0.71。相比其他DenseNet网络，DenseNet201在准确率上提高了5%左右。研究表明DenseNet201在处理大规模乳腺癌图像数据集时，具备更强大的特征提取能力，能更好地适应复杂的数据模式和关系，从而提高了乳腺癌检测的准确性和效率。

Abstract

This study takes breast cancer as the object. Through image processing and deep learning technology

DenseNet201 network is used to analyze and classify breast pathological images. The dataset included 162 breast cancer specimens

of which DenseNet121 was used for experimental comparison. Experimental results showed that DenseNet201 performed better in breast cancer detection than DenseNet121. Through 20 training epochs

the overall accuracy reached 82%

the accuracy of category without invasive ductal carcinoma was 90%

the recall rate was 84%

and the

score was 0.87. With invasive ductal carcinoma category accuracy of 66%

recall rate of 77%

score of 0.71. Compared to other DenseNet networks

DenseNet201 improves accuracy by about 5%. When processing large-scale breast cancer image data sets

DenseNet201 has more powerful feature extraction capabilities and can better adapt to complex data patterns and relationships

thus improving the accuracy and efficiency of breast cancer detection.

关键词

乳腺癌DenseNet病理图像深度学习

Keywords

breast cancerDenseNetpathological imagedeep learning

references

BURSTEIN H J, CURIGLIANO G, THÜRLIMANN B, et al. Customizing local and systemic therapies for women with early breast cancer: The St. Gallen International Consensus Guidelines for treatment of early breast cancer 2021[J]. Annals of Oncology, 2021, 32(10): 1216-1235.

WILKINSON L, GATHANI T. Understanding breast cancer as a global health concern[J]. The British Journal of Radiology, 2022, 95(1130): 20211033.

KATSURA C, OGUNMWONYI I, KANKAM H K, et al. Breast cancer: Presentation, investigation and management[J]. British Journal of Hospital Medicine, 2022, 83(2): 1-7.

ELSTON C W, ELLIS I O, PINDER S E. Pathological prognostic factors in breast cancer[J]. Critical Reviews in Oncology/Hematology, 1999, 31(3): 209-223.

UDUPA J K, HERMAN G T. Medical image reconstruction, processing, visualization, and analysis: The MIPG perspective. Medical Image Processing Group[J]. IEEE Transactions on Medical Imaging, 2002, 21(4): 281-295.

CSERNI G, AMENDOEIRA I, APOSTOLIKAS N, et al. Pathological work-up of sentinel lymph nodes in breast cancer. Review of current data to be considered for the formulation of guidelines[J]. European Journal of Cancer, 2003, 39(12): 1654-1667.

ZHANG Y N, XIA K R, LI C Y, et al. Review of breast cancer pathologigcal image processing[J]. BioMed Research International, 2021, 2021: 1994764.

ZHU Y, NEWSAM S. DenseNet for dense flow[EB/OL]. (2017)[2023]. https://arxiv.org/abs/1707.06316v1https://arxiv.org/abs/1707.06316v1.

AL-MASNI M A, AL-ANTARI M A, PARK J M, et al. Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system[J]. Computer Methods and Programs in Biomedicine, 2018, 157: 85-94.

STOLLMAYER R, BUDAI B K, TÓTH A, et al. Diagnosis of focal liver lesions with deep learning-based multi-channel analysis of hepatocyte-specific contrast-enhanced magnetic resonance imaging[J]. World Journal of Gastroenterology, 2021, 27(35): 5978-5988.

PAPANDRIANOS N, PAPAGEORGIOU E, ANAGNOSTIS A, et al. A deep-learning approach for diagnosis of metastatic breast cancer in bones from whole-body scans[J]. Applied Sciences, 2020, 10(3): 997.

刘金平, 吴娟娟, 张荣, 等. 基于结构重参数化与多尺度深度监督的COVID-19胸部CT图像自动分割[J]. 电子学报, 2023, 51(5): 1163-1171.

谢娟英, 张凯云. SOSNet: 一种非对称编码器-解码器结构的非小细胞肺癌CT图像分割模型[J]. 电子学报, 2024, 52(3): 824-837.

浏览量

107

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

基于GoogLeNet的乳腺癌超声图像分类

深度学习技术在医学影像分析中的应用与展望

深度学习下的小样本玉米叶片病害识别研究

真实世界超分辨率—语义分割联合框架研究