医学X射线,MRI和超声波图像分类任务的深度学习方法:范围审查
Hafsa Laçi1, Kozeta Sevrani1, Sarfraz Iqbal2
1Department of Statistics and Applied Informatics, Faculty of Economy, University of Tirana, Tirana, Albania.
BMC medical imaging
|May 7, 2025
概括
深度学习有效地从医学图像 (如X射线和MRI) 中对疾病进行分类. 本综述探讨了深度学习模型,数据集和用于准确医疗图像分析的技术.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 放射学和诊断成像学 放射学和诊断成像学
背景情况:
- 医学图像分析是复杂和耗时的,特别是手动分类.
- 深度学习为图像分类,细分和转移学习提供先进的算法.
- 精确解释X射线,MRI和超声波图像对于疾病诊断至关重要.
研究的目的:
- 进行对深度学习用于使用X射线,MRI和超声波进行疾病分类的应用进行范围审查.
- 探索医疗图像分类中的各种深度学习模型,数据集和方法.
- 确定高精度模型,并讨论该领域的局限性和未来方向.
主要方法:
- 根据PRISMA-ScR指南进行的范围审查.
- 对医学图像分类的深度学习研究进行系统的搜索和综合.
- 分析数据集,预处理/增强技术和深度学习模型架构.
主要成果:
- 用于医学成像深度学习的数据集和预处理技术的概述特征.
- 详细审查应用到X射线,MRI和超声波用于疾病分类的深度学习模型.
- 突出显示了实现最高精度的模型,包括它们的优点和配置.
结论:
- 深度学习显示了自动化和提高医学图像分类准确性的重大前景.
- 需要进一步的研究来解决局限性,并优化深度学习模型,用于各种医学成像应用.
- 数据集的标准化和模型配置的报告可以提高可重现性和比较性.
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