利用机器学习分析放射影像检测乳腺癌:系统回顾

Kristell Yukie Jimenez Ayala
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引用次数: 0

摘要

乳腺癌是一种影响许多妇女的疾病,甚至会导致死亡;这是一种未被及时发现的病例,可能是由于在分析放射图像时的人为失误或未按时前往医疗中心。为此,建议使用机器学习(ML)分析放射图像,作为放射科医生的辅助工具,以降低误诊率。在研究信息时,我们发现这项技术在卫生领域有很多好处,但也有局限性或缺点。本文的重要性在于证明了临床测试和所使用方法的细节都不够充分;应更多地断言人工智能是在做出诊断的那一刻定义的,这不会产生有关有效性的结论性结果,因此会造成对医生的不信任,而有些人可能更愿意使用深度学习(DL)来检测乳腺癌,因为与机器学习相比,深度学习有更多的实际测试和更少的局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Breast Cancer through the Analysis of Radiographic Images Using Machine Learning: A Systematic Review
Breast cancer is an illness that affects many women and can cause even death; this is a case of not being detected on time, which could be due to a human error during the analysis of radiographic images or not going on time in a health center. For this, using machine learning (ML) to analyze radiographic images is proposed as a support tool for radiologists aiming to reduce false diagnostic rates. While researching information, it was detected that this technology has many benefits in the health area; however, it also has limitations or disadvantages. The importance of this paper is to demonstrate that there are not enough clinical tests nor details about the methodologies that were used; there should be more to assert that ML is defined at the moment of making a diagnosis, which generates no conclusive results regarding effectiveness and therefore creates mistrust in doctors, and some people might rather use deep learning (DL) for its application in the detection of breast cancer because DL has more practical tests and fewer limitations than machine learning.
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