Convolutional neural networks for automatic detection of breast pathologies

被引：1

作者：

Gonzalez-Fraga, Jose A. ^{[1
]}

Kober, Vitaly ^{[2
,3
]}

Gutierrez-Lopez, Everardo ^{[1
]}

Gonzalez-Sarabia, Alejandro ^{[1
]}

机构：

[1] Univ Autonoma Baja California, Fac Ciencias, Ensenada 22860, Baja California, Mexico

[2] CICESE, Dept Comp Sci, Ensenada 22860, Baja California, Mexico

[3] Chelyabinsk State Univ, Dept Math, Chelyabinsk, Russia

来源：

APPLICATIONS OF DIGITAL IMAGE PROCESSING XLV | 2022年 / 12226卷

基金：

俄罗斯科学基金会;

关键词：

Mammography; Breast cancer; Convolutional neural networks; Transfer learning;

D O I：

10.1117/12.2633449

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

Breast cancer in women is a worldwide health problem that has a high mortality rate. A strategy to reduce breast cancer mortality in women is to implement preventive programs such as mammography screening for early breast cancer diagnosis. In this presentation, a method for automatic detection of breast pathologies using a deep convolutional neural network and a class activation map is proposed. The neural network is pretrained on the regions of interest in order to modify the output layers to have two output classes. The proposed method is compared with different CNN models and applied to classify the public dataset Curated Breast Imaging Subset of DDSM (CBIS-DDSM).

引用

页数：5

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