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Brain Imaging

Brain 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...

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Updated: Jul 18, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

基于MRI的有效组合框架用于预测人类脑瘤.

Farhana Khan¹, Shahnawaz Ayoub¹, Yonis Gulzar²

¹Glocal School of Science and Technology, Glocal University, Delhi-Yamunotri Marg (State Highway 57), Mirzapur Pole 247121, India.

Journal of imaging

|August 25, 2023

概括

此摘要是机器生成的。

早期发现脑瘤对于生存至关重要. 这项研究在MRI扫描上使用深度学习和机器学习,在识别瘤方面达到95.9%的准确性,显著改善了诊断.

关键词:

大脑瘤是个大脑瘤卷积神经网络的神经网络.整体方法是一个整体的方法.磁共振成像技术的使用

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Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

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Published on: August 16, 2020

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

相关实验视频

Last Updated: Jul 18, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Whole-brain Segmentation and Change-point Analysis of Anatomical Brain MRI—Application in Premanifest Huntington's Disease

Published on: June 9, 2018

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:

由于死亡率快,早期诊断脑瘤至关重要.
自动化方法对于有效及时检测至关重要.
磁共振成像 (MRI) 是大脑成像的一个关键模式.

研究的目的:

利用MRI数据开发一种用于早期脑瘤检测的自动化方法.
利用深度学习和机器学习来提高分类准确性.
为了提高区分瘤患者与正常个体的精度.

主要方法:

利用深层卷积神经网络 (CNN) 来从MRI扫描中全面提取特征.
用五种不同的机器学习 (ML) 模型进行了实验,用于脑瘤分类.
提出了一个组合模型 (XG-Ada-RF),结合了极端梯度提升,Ada-Boost和随机森林.

主要成果:

整体模型在瘤检测方面达到95.9%,在正常病例中达到94.9%的高精度.
深度卷积特征显著提高了瘤和非瘤分类的精度.
拟议的整体方法与单个ML模型相比显示出更高的性能.

结论:

深度卷积特征和整体ML分类器的集成为早期脑瘤检测提供了一个高度准确的自动化解决方案.
这种方法显著提高了诊断能力,有可能提高患者的生存率.
这项研究强调了医疗图像分析中集合方法在复杂诊断,如脑瘤等复杂诊断中的有效性.