检索结果-南通市图书馆

A Breast Density Classification System for Mammography Considering Reliability Issues in Deep Learning

Open Journal of Medical Imaging 2023年第3期13卷 63-83页

作者： Eri Matsuyama megumi takehara Noriyuki Takahashi Haruyuki Watanabe Faculty of Informatics University of Fukuchiyama Kyoto Japan Breast Center Dokkyo Medical University Hospital Tochigi Japan School of Hearth Sciences Fukushima Medical University Fukushima Japan School of Radiological Technology Gunma Prefectural College of Health Sciences Gunma Japan

In a convolutional neural network (CNN) classification model for diagnosing medical images, transparency and interpretability of the model’s behavior are crucial in addition to high classification accuracy, and it is highly important to explicitly demonstrate them. In this study, we constructed an interpretable CNN-based model for breast density classification using spectral information from mammograms. We evaluated whether the model’s prediction scores provided reliable probability values using a reliability diagram and visualized the basis for the final prediction. In constructing the classification model, we modified ResNet50 and introduced algorithms for extracting and inputting image spectra, visualizing network behavior, and quantifying prediction ambiguity. From the experimental results, our proposed model demonstrated not only high classification accuracy but also higher reliability and interpretability compared to the conventional CNN models that use pixel information from images. Furthermore, our proposed model can detect misclassified data and indicate explicit basis for prediction. The results demonstrated the effectiveness and usefulness of our proposed model from the perspective of credibility and transparency.

关键词： Explainable AI t-SNE Entropy Wavelet Transform Mammogram

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Using a Wavelet-Based and Fine-Tuned Convolutional Neural Network for Classification of Breast Density in Mammographic Images

引用

Open Journal of Medical Imaging 2020年第1期10卷 17-29页

作者： Eri Matsuyama megumi takehara Du-Yih Tsai Department of Radiological Sciences International University of Health and Welfare Tochigi Japan Breast Center Dokkyo Medical University Hospital Tochigi Japan Department of Biomedical Engineering Hungkuang University Taichung City Taiwan China

Classification of breast density is significantly important during the process of breast diagnosis. The purpose of this study was to develop a useful computer-ized tool to help radiologists determine the patient’s breast density category on the mammogram. In this article, we presented a model for automatically classi-fying breast densities by employing a wavelet transform-based and fine-tuned convolutional neural network (CNN). We modified a pre-trained AlexNet model by removing the last two fully connected (FC) layers and appending two newly created layers to the remaining structure. Unlike the common CNN-based methods that use original or pre-processed images as inputs, we adopted the use of redundant wavelet coefficients at level 1 as inputs to the CNN model. Our study mainly focused on discriminating between scattered density and heterogeneously dense which are the two most difficult density cat-egories to differentiate for radiologists. The proposed system achieved 88.3% overall accuracy. In order to demonstrate the effectiveness and usefulness of the proposed method, the results obtained from a conventional fine-tuning CNN model was compared with that from the proposed method. The results demon-strate that the proposed technique is very promising to help radiologists and serve as a second eye for them to classify breast density categories in breast cancer screening.

关键词： Mammography Mammary Gland Density CNN Wavelet Transform

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：