Classification of Breast Cancer Ultrasound Images Based on GoogLeNet

HAN Yang; SUN Yue; GUO Jin-xing; CHEN Guang-xin; GAO Ming-ze

doi:10.3969/j.issn.2096-6091.2023.22.004

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Classification of Breast Cancer Ultrasound Images Based on GoogLeNet

RESEARCH PAPER | 更新时间：2025-03-26

- Classification of Breast Cancer Ultrasound Images Based on GoogLeNet
- New Generation of Information Technology Vol. 6, Issue 22, Pages: 24-28(2023)
- 作者机构：
  
  1.牡丹江医科大学医学影像学院，黑龙江牡丹江157011
  2.牡丹江医科大学附属红旗医院，黑龙江牡丹江157011
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.2096-6091.2023.22.004
  CLC： TP18;R737.9
- Published：30 November 2023
- 稿件说明：
移动端阅览
韩杨, 孙悦, 郭金兴, 等. 基于GoogLeNet的乳腺癌超声图像分类[J]. 新一代信息技术, 2023, 6(22): 24-28

HAN Yang, SUN Yue, GUO Jin-xing, et al. Classification of Breast Cancer Ultrasound Images Based on GoogLeNet[J]. New Generation of Information Technology, 2023, 6(22): 24-28
韩杨, 孙悦, 郭金兴, 等. 基于GoogLeNet的乳腺癌超声图像分类[J]. 新一代信息技术, 2023, 6(22): 24-28 DOI： 10.3969/j.issn.2096-6091.2023.22.004.

HAN Yang, SUN Yue, GUO Jin-xing, et al. Classification of Breast Cancer Ultrasound Images Based on GoogLeNet[J]. New Generation of Information Technology, 2023, 6(22): 24-28 DOI： 10.3969/j.issn.2096-6091.2023.22.004.

摘要

本研究旨在开发一种基于深度学习的模型，用于对乳腺癌超声图像进行准确分类，以提升乳腺癌早期诊断的精确性。采用了GoogleNet深度学习模型，并在Kaggle提供的乳腺癌超声图像数据集上进行了训练和验证。通过调整模型参数和结构，实现了对良性、恶性和正常乳腺超声图像的有效区分。实验结果表明，所提出的深度学习模型在乳腺癌超声图像分类任务中展现出卓越的性能，具有高精确度、召回率和

分数。该模型有望成为辅助乳腺癌早期诊断的强有力工具，为临床医生提供更为

精确和可靠的诊断依据，从而对乳腺癌患者的治疗和预后产生积极影响。

Abstract

This study aims to develop a deep learn-based model for the accurate classification of ultrasound images of breast cancer in order to improve the accuracy of early diagnosis of breast cancer. The GoogleNet deep learning model was used in this study

and was trained and verified on the breast cancer ultrasound image dataset provided by Kaggle. By adjusting the parameters and structure of the model

benign

malignant and normal breast ultrasound images were effectively distinguished.The experimental results show that the proposed deep learning model has excellent performance in breast cancer ultrasound image classification tasks

with high accuracy

recall rate and

scores. This model is expected to become a powerful tool to assist the early diagnosis of breast cancer

and provide clinicians with more accurate and reliable diagnostic basis

so as to have a positive impact on the treatment and prognosis of breast cancer patients.

关键词

Keywords

references

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Related Institution

Hongqi Hospital Affiliated with Mudanjiang Medical University

Basic Medical College of Mudanjiang Medical University

Medical Imaging College of Mudanjiang Medical University

Chongqing Communication Design Institute Co., Ltd

Henan Agricultural University ， College of Information and Mangement Science

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