使用MRI图像 (2020至2024) 进行脑瘤检测和分类的深度学习方法:系统性审查
Sara Bouhafra1, Hassan El Bahi2
1Faculty of Sciences and Techniques, Department of Computer Science, L2IS Laboratory, Cadi Ayyad University, Marrakesh, Morocco. s.bouhafra.ced@uca.ac.ma.
Journal of imaging informatics in medicine
|September 30, 2024
概括
本文审查了使用MRI图像进行脑瘤检测和分类的深度学习. 它强调了人工智能辅助诊断的进步,以提高患者存活率.
科学领域:
- 医学成像分析 医学成像分析
- 在瘤学中使用人工智能
- 神经科学是一个神经科学.
背景情况:
- 大脑瘤的特点是细胞生长不受控制,需要早期检测以改善患者的治疗结果.
- 放射科医生面临着繁重的工作负载,增加了对先进诊断工具的需求,以尽量减少错误.
- 计算机辅助诊断和人工智能在帮助医疗专业人员方面显示出重大前景.
研究的目的:
- 系统地审查基于深度学习的脑瘤检测和使用MRI进行分类的方法.
- 总结现有的局限性和该领域的关键进展.
- 提供分析性比较,并概述未来的研究方向.
主要方法:
- 2020年至2024年1月期间发表的60篇研究文章的系统文献综述.
- 分析深度学习技术,包括转移学习,自动编码器,转换器和注意力机制.
- 专注于用于磁共振成像 (MRI) 进行脑瘤分析的方法.
主要成果:
- 深度学习方法在脑瘤检测和MRI扫描的分类方面显示出有希望的结果.
- 介绍了各种人工智能技术及其有效性的全面概述.
- 关键发现提供了关于自动脑瘤诊断现状和潜力的见解.
结论:
- 人工智能和深度学习对于提高脑瘤诊断的准确性和效率至关重要.
- 该评论为神经瘤学领域的研究人员和临床医生提供了宝贵的资源.
- 进一步的研究旨在完善用于临床应用的自动化技术.
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