癌症诊断系统的人工智能新兴研究趋势:全面审查
Sagheer Abbas1, Muhammad Asif2, Abdur Rehman3
1Department of Computer Science, Prince Mohammad Bin Fahd University, Al-Khobar, KSA.
Heliyon
|September 12, 2024
概括
机器学习通过提高准确性和速度显著提高了癌症诊断. 这篇评论探讨了先进的技术,数据处理和诸如可解释性等挑战,指导了人工智能驱动的癌症检测的未来发展.
科学领域:
- 在瘤学瘤学.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 机器学习 (ML) 为癌症诊断中分析复杂的医疗数据提供了先进的功能.
- 目前的ML方法表明癌症检测的准确性和速度有所提高.
- 解决大型数据集中的挑战对于在瘤学中有效应用ML至关重要.
研究的目的:
- 对癌症诊断系统的现代机器学习技术进行全面审查.
- 评估算法,数据预处理,特征选择和癌症诊断中的模型评估.
- 讨论挑战和未来方向,包括可解释的人工智能 (XAI) 和个性化医学.
主要方法:
- 审查监督,无监督,深度学习和联合学习方法.
- 分析用于成像,基因组学和临床记录的数据采集和预处理.
- 检查特征提取,模型训练,评估指标和性能比较.
主要成果:
- ML技术在提高癌症检测准确性和速度方面表现出有效性.
- 不同的ML算法和数据处理方法适用于不同类型的癌症.
- 可解释性AI (XAI) 技术正在出现,以提高透明度和临床解释.
结论:
- 机器学习对于推进癌症诊断系统至关重要.
- 解决可解释性,数据限制和伦理方面的挑战对于临床采用至关重要.
- 未来的方向包括个性化医学,联合学习和深度学习的进步.
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