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Neural Control of Respiration

Neural Control of Respiration

The neural regulation of respiration is a meticulously coordinated process primarily controlled by the respiratory centers located within the brainstem. These centers, composed of specialized neurons, transmit nerve impulses that control the contraction and relaxation of our respiratory muscles.
Respiratory Centers in the Brainstem
Two primary areas comprise the respiratory center: the medullary respiratory center in the medulla oblongata and the pontine respiratory group in the pons. The...

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Updated: May 7, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

肺部细分与轻量级卷积注意力残余U-Net

Meftahul Jannat¹, Shaikh Afnan Birahim², Mohammad Asif Hasan¹

¹Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi 6204, Bangladesh.

Diagnostics (Basel, Switzerland)

|April 12, 2025

概括

这项研究引入了一个新的深度学习模型,用于胸部X射线中精确的肺部细分,帮助早期发现疾病. 轻量级残留U-Net实现了高精度,提高了放射科医生的诊断能力.

关键词:

在CXR图像中使用CXR图像.卷积块注意力模块的注意力模块子的损失子的损失轻量级的残余U-Net可以使用.肺部细分肺部的细分

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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
计算机视觉计算机视觉

背景情况:

胸部放射 (CXR) 分析复杂且耗时,通常需要识别多个异常.
精确的肺部细分对于提高CXR分析中的深度学习 (DL) 模型的效率和性能至关重要.
高风险肺部疾病的早期检测需要在CXR图像中精确识别肺部区域.

研究的目的:

在CXR图像中开发一种基于DL的方法,用于精确的肺口罩细分.
协助放射科医生早期识别高风险肺部疾病的迹象.
提出一种新,高效,准确的肺部细分模型.

主要方法:

提出了一种新的轻量级残余U-Net架构,集成卷积块注意模块 (CBAM) 和Atrous空间金字塔聚合 (ASPP).
该模型包含324万个参数,使用LeakyReLU激活进行训练,并使用Dice损失函数进行优化.
该技术利用DL和注意力机制来增强肺部细分.

主要成果:

拟议的模型在基准数据集上获得了高的子得分:98.72% (JSRT),97.49% (SZ) 和99.08% (MC).
性能在肺部细分精度方面超过了现有的最先进模型.
泄漏ReLU激活和子损失功能表现出卓越的有效性.

结论:

开发的DL模型显著提高了CXR图像中的肺部细分精度.
该模型的效率和精度有助于早期检测严重的肺部疾病.
这种方法代表了利用人工智能用于医学诊断的进步.