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Brain Imaging01:14

Brain Imaging

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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...
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DTDO:驾驶培训开发优化启用深度学习方法用于使用MRI进行脑瘤分类.

Vadamodula Prasad1, Issac Diana Jeba Jingle2, Gopalsamy Venkadakrishnan Sriramakrishnan3

  • 1Department of Computer Science & Engineering, Lendi Institute of Engineering & Technology, Jonnada, India.

Network (Bristol, England)
|May 27, 2024
PubMed
概括

这项研究介绍了DTDO-ZFNet,这是一种用于在MRI扫描中检测脑瘤的新方法. 新方法显著提高了检测准确度,减少了假阴性,有助于更早的诊断.

关键词:
一个大脑瘤.儿童绘画发展优化 (CDDO)驾驶培训发展优化 (DTDO) 项目磁共振成像 (MRI) 是一种磁共振成像技术.

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 生物医学工程 生物医学工程

背景情况:

  • 由于大小,形状和边界不规则的变化,大脑瘤存在检测挑战.
  • 准确的细分和分类对于有效的脑瘤诊断和治疗计划至关重要.

研究的目的:

  • 引入和评估DTDO-ZFNet模型,以使用MRI增强脑瘤检测.
  • 为了解决瘤划分的复杂性,并提高诊断准确性.

主要方法:

  • 磁共振成像 (MRI) 数据的预处理.
  • 使用SegNet进行瘤细分,并使用DTDO (DTBO和CDDO的混合体) 进行优化.
  • 包括GIST,PCA-NGIST,统计,Haralick,SLBT和CNN特征在内的特征提取.
  • 使用经过DTDO培训的ZFNet进行瘤分类.

主要成果:

  • 该DTDO-ZFNet实现了0.944.4的高精度.
  • 证明了0.936的正预测值 (PPV) 和0.939.93的真正率 (TPR).
  • 实现了0.937的负预测值 (NPV),低假负率 (FNR) 为0.061%.

结论:

  • 拟议的DTDO-ZFNet模型为通过MRI扫描检测脑瘤提供了强大而准确的解决方案.
  • 该方法有效地克服了与不规则的瘤边界和变异相关的挑战.
  • 与现有方法相比,DTDO-ZFNet显示出更高的性能,为改善临床诊断铺平了道路.