审查使用深度学习技术的胸部病变检测系统
Arshia Rehman1, Ahmad Khan1, Gohar Fatima2
1COMSATS University Islamabad, Abbottabad-Campus, Abbottabad, Pakistan.
概括
深度学习显著提高了使用计算机辅助方法自动检测胸部疾病. 本综述综合了胸部放射学分析深度学习的最新进展,提供了对当前趋势和未来方向的见解.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 计算机辅助诊断 计算机辅助诊断
背景情况:
- 胸部放射是诊断胸部疾病的主要,具有成本效益的工具.
- 专家放射科医生传统上解释放射图,但计算机辅助方法提高了准确性和速度.
- 深度学习已经彻底改变了对胸部病理的自动检测和分析.
研究的目的:
- 进行计算机辅助胸部病理检测系统的全面审查和技术评估.
- 通过深度学习,综合单个和多种病理检测的最新技术.
- 介绍自动化胸部疾病分析方法的分类.
主要方法:
- 在过去五年中发表的基于深度学习的计算机辅助检测系统的系统审查.
- 讨论图像采集,数据集预处理和特征提取技术.
- 分析深度学习模型架构及其数学基础.
主要成果:
- 基于贡献,数据集,方法和绩效指标的系统的详细比较.
- 确定单种和多种病理检测的关键进展.
- 综合该领域当前的趋势和挑战.
结论:
- 深度学习模型显示了自动胸部病理检测的重大前景.
- 数据集和评估指标的标准化对于未来的进展至关重要.
- 未来的研究应该专注于解决当前的挑战,并探索新的深度学习架构.
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