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相关概念视频

Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Updated: May 12, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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使用深度特征融合用于自动白血病分类:医疗事物互联网-启用深度学习框架

Md Manowarul Islam1, Habibur Rahman Rifat1, Md Shamim Bin Shahid1

  • 1Department of Computer Science and Engineering, Jagannath University, Dhaka 1100, Bangladesh.

Sensors (Basel, Switzerland)
|July 13, 2024
PubMed
概括
此摘要是机器生成的。

这项研究介绍了一种使用深度学习的AI驱动框架,可以从血液图像中自动检测白血病. 这种新型的融合模型实现了高精度,为急性淋巴细胞白血病 (ALL) 提供了更快,更有效的诊断工具.

关键词:
这就是DenseNet-121的特点.在VGG16中,VGG16是VGG16中的一个.功能融合功能融合功能医疗事物互联网的互联网.这种白血病是白血病.细分化 细分化的细分化转移学习转移学习

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科学领域:

  • 医学诊断 医学诊断 医学诊断
  • 医疗保健中的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 诊断急性淋巴细胞白血病 (ALL) 是具有挑战性的,需要耗时和昂贵的专家测试.
  • 早期发现ALL对于及时和有效的治疗启动至关重要.
  • 人工智能 (AI) 和物联网 (IoT) 的进步为诊断提供了新的可能性.

研究的目的:

  • 引入基于人工智能的医疗物联网 (IoMT) 框架,用于从外周血液涂抹 (PBS) 图像中自动检测白血病.
  • 开发和评估基于深度学习的融合模型,用于准确的ALL分类.
  • 为了提高白血病诊断的速度和效率.

主要方法:

  • 开发了一个融合深度学习模型,利用两个输入道:原始和细分的PBS图像.
  • VGG16和DenseNet-121分别用于从原始和细分图像中提取特征.
  • 该模型在89个个人的6512张图像上进行了训练,并对分类性能进行了评估.

主要成果:

  • 拟议的融合模型实现了高诊断精度 (99.89%),精度 (99.80%),和回忆 (99.72%).
  • 该模型与几种最先进的卷积神经网络 (CNN) 模型相比,表现出更高的性能.
  • 开发了一个Web应用程序 (Beta版本) 来模拟白血病检测方法.

结论:

  • 开发的基于人工智能的IoMT框架和融合模型显示出对准确高效的白血病检测有很大的潜力.
  • 这种方法有可能挽救生命,减少诊断工作.
  • 这些发现对推进生物医学研究中的计算机辅助白血病检测有意义.