使用机器学习技术的计算机辅助诊断

Sowmya Prakash, K. Harshitha, A.O. Charitha, C. Janvitha, K. Indu
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引用次数: 0

摘要

计算机辅助诊断(CAD)是医学分析的一个领域,在大范围内迅速发展,并变得越来越复杂。计算机辅助最近对诊断产生了很大的兴趣,因为不准确的医疗诊断可能导致严重误导的治疗。机器学习(ML)是计算机辅助诊断检查的关键组成部分。一个简单的方程式无法准确地识别人体的某个器官。因此,模式识别需要从实例中学习。使用模式检测技术和机器学习(ML)技术,可以提高医学领域诊断疾病和做出适当治疗决策的准确性,客观地做出决策对他们来说很重要。机器学习(ML)为开发改进的自动化算法提供了可靠的方法,用于分析高维、多模态的生物数据。这篇综述文章比较了用于各种疾病检测的各种机器学习方法和算法。分析了机器学习方法的范围以及在医学诊断和决策中使用的方法
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Computer-Aided Diagnosis using Machine Learning Techniques
Computer-aided diagnosis (CAD), a field of medical analysis, is rapidly advancing in a large range and is becoming more complex. Computer-aided recently, there has been a lot of interest in diagnostics for the reason inaccurate medical diagnosis could result in significantly misleading therapies. Machine learning (ML) is a key component of computer-aided diagnostic examinations. A simple equation cannot identify an organ in the body with the required accuracy. Therefore, pattern recognition requires learning from examples. Using pattern detection techniques and machine learning (ML) techniques, it is possible to improve the accuracy of diagnosing diseases and making appropriate treatment decisions in the medical field It is important to them that decisions are made objectively. Machine Learning (ML) provides a reliable approach to the development of improved, automated algorithms for the analysis of high- dimensional, multi-modal biological data. This survey article compares various machine learning approaches and algorithms for a variety of diseases' detection. The range of Machine Learning (ML) methods and methods utilized in the medical diagnosis and decision- making are analyzed
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