Machine Learning Techniques for Challenging Tumor Detection and Classification in Breast Cancer

机译：机器学习技术可挑战乳腺癌的肿瘤检测和分类

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Breast cancer is the foremost common invasive cancer among ladies and is the primary cause of mortality from cancer around the world. To avoid the tall mortality, early detection and treatment are essential. Breast screening which uses noninvasive manner to assess tissue properties plays an imperative role in early designation. In any case, the huge information volume makes the examination become long, time-consuming and inoperable. Machine learning techniques and image processing are trending to investigate different tissue characterization and tumor appearance in radiology for automatic malignancy classification. This paper provide comparison of two supervised classification techniques for angiogenesis detection in computed tomography laser mammography image which is the major sign of high hemoglobin concentration and breast cancer.

机译：乳腺癌是女性中最常见的浸润性癌症，并且是全世界癌症致死的主要原因。为了避免很高的死亡率，早期发现和治疗是必不可少的。使用无创方式评估组织性质的乳腺癌筛查在早期指定中起着至关重要的作用。无论如何，巨大的信息量使检查变得冗长，费时且无法进行。机器学习技术和图像处理正在趋向于研究放射学中用于自动恶性分类的不同组织特征和肿瘤外观。本文比较了计算机断层扫描乳腺X线摄影图像中血管生成检测的两种监督分类技术，这是高血红蛋白浓度和乳腺癌的主要标志。

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来源
《Conference on Artificial Intelligence and Robotics;Asia-Pacific International Symposium》|2018年|19-24|共6页
会议地点 Kish Island(IR)
作者
Afsaneh Jalalian; Babak Karasfi;
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作者单位

Raja University Qazvin Iran;

Faculty of Computer and Information Technology Engineering Islamic Azad University Qazvin Iran;

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关键词
Three-dimensional displays; Feature extraction; Breast cancer; Shape; Lesions;

机译：三维显示器；特征提取;乳腺癌;形状;病灶;

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专利

1. Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection [J] . Edén A. Alanís-Reyes, José L. Hernández-Cruz, Jesús S. Cepeda, Journal of Cancer Therapy . 2012,第6期

机译：机器学习技术在乳腺癌计算机辅助检测肿块和微钙化簇分类中的应用分析
2. A comparative study of breast cancer tumor classification by classical machine learning methods and deep learning method [J] . Yadavendra, Satish Chand Machine Vision and Applications . 2020,第6期

机译：古典机器学习方法和深层学习方法对乳腺癌肿瘤分类的对比研究
3. Breast Cancer Classification Using Various Machine Learning Techniques [J] . Advances in Natural and Applied Sciences . 2015,第3期

机译：使用各种机器学习技术对乳腺癌进行分类
4. Machine Learning Techniques for Challenging Tumor Detection and Classification in Breast Cancer [C] . Afsaneh Jalalian, Babak Karasfi Conference on Artificial Intelligence and Robotics . 2018

机译：用于攻击肿瘤检测和乳腺癌分类的机器学习技术
5. Development of Segmentation Algorithms and Machine Learning Classification Methods for Characterization of Breast and Spine Tumors on MRI. [D] . Wang, Xinxin. 2017

机译：MRI乳腺和脊柱肿瘤特征分割算法和机器学习分类方法的发展。
6. Implementing Multilabeling ADASYN and ReliefF Techniques for Classification of Breast Cancer Diagnostic through Machine Learning: Efficient Computer-Aided Diagnostic System [O] . Taha Muthar Khan, Shengjun Xu, Zullatun Gull Khan, 2021

机译：通过机器学习实施乳腺癌诊断分类的多标签Adasyn和Creieff技术：高效的计算机辅助诊断系统
7. Analysis of Machine Learning Techniques Applied to the Classification of Masses and Microcalcification Clusters in Breast Cancer Computer-Aided Detection [O] . Edén A. Alanís-Reyes, José L. Hernández-Cruz, Jesús S. Cepeda, 2012

机译：机器学习技术在乳腺癌计算机辅助检测中肿块和微钙化簇分类中的应用分析

Machine Learning Techniques for Challenging Tumor Detection and Classification in Breast Cancer

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