基于深度学习的脑瘤细分和使用MRI检测的系统审查:过去的见解,当前的技术和未来的趋势
Krupa Chary Pasunoori1, Ch Rajendra Prasad1, K Raj Kumar1
1Department of ECE, SR University, Warangal, 506371, Telangana, India.
Computational biology and chemistry
|October 5, 2025
概括
使用磁共振成像 (MRI) 和深度学习模型早期检测脑瘤可显著改善患者的生存率. 这篇评论分析了用于强大的脑瘤细分和MRI扫描检测的深度学习技术.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 在瘤学瘤学.
背景情况:
- 脑瘤是全球成年人死亡的主要原因.
- 早期诊断脑瘤对于改善患者生存率和治疗结果至关重要.
- 磁共振成像 (MRI) 为脑瘤检测和分化提供了全面的数据.
研究的目的:
- 提供关于脑瘤细分和检测技术的全面概述.
- 分析深度学习模型在处理大量MRI数据中用于脑瘤分析的应用.
- 突出当前的挑战和该领域的研究差距.
主要方法:
- 对基于深度学习的模型进行大脑瘤识别和细分的审查.
- 时间分析,以验证各种技术的稳定性.
- 讨论数据集细节,绩效评估和模拟工具.
主要成果:
- 深度学习模型在分析MRI数据以检测脑瘤方面表现出有效性.
- 讨论了标准深度学习方法的优点和局限性.
- 确定了当前模型中的关键挑战和研究差距.
结论:
- 深度学习为使用MRI进行脑瘤细分和检测提供了一种强大的方法.
- 需要进一步的研究来应对现有挑战并改善模型性能.
- 优化模型可以提高早期诊断和脑恶性瘤患者的结果.
相关概念视频
01:24Magnetic Resonance Imaging
Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...
01:14Brain Imaging
Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).