利用机器学习方法诊断乳腺癌

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最近几年，机器学习方法在诊断领域，特别是对乳腺癌的检测受到越来越多的关注。

文章从三个方面分析了当前致力于在乳腺癌诊断中使用机器学习方法的研究：用于解决现代乳腺癌诊断中出现的辅助任务，作为初步诊断决策的患者病情智能评估，以及确定乳腺癌的风险因素。文章从三个方面分析了机器学习方法在乳腺癌诊断中的应用现状：解决现代乳腺癌诊断中出现的辅助课题，智能评估患者病情初步诊断决策，以及确定乳腺癌的风险因素。

分析表明，利用机器学习方法诊断乳腺癌为提高诊断的准确性和效率提供巨大的可能性，并且还可以完成其他额外任务。

文献分析结果，确定了用作机器学习方法中输入数据的特征。今后，收集到的信息将用于构建一个利用机器学习方法诊断乳腺癌的特征系统。

作者简介

Kirill S. Dyomin

Volgograd State University

编辑信件的主要联系方式.
Email: diominkirill@yandex.ru
ORCID iD: 0009-0002-4571-3437
俄罗斯联邦, Volgograd

Ilya V. Germashev

Volgograd State University

Email: germashev@volsu.ru
ORCID iD: 0000-0001-5507-8508
SPIN 代码: 2489-2628

Dr. Sci. (Engineering)

俄罗斯联邦, Volgograd

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补充文件

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2. Fig. 1. Example of tumor localization [3].

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3. Fig. 2. Visualization of two patient examples [14]: a - the low-sound region inside the tumor is valuable for predicting the status of perivascular lymph nodes; b - tumor border.

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4. Fig. 3. Example of a thermogram of a healthy patient.

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5. Fig. 4. Location of points for obtaining temperatures.

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6. Fig. 5. Frequency of use of machine learning models, %.

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