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相关概念视频

Classification of Epithelial Tissues: Overview01:22

Classification of Epithelial Tissues: Overview

Epithelial tissues are classified according to the shape of the cells and the number of cell layers formed. Cell shapes can be squamous (flattened and thin), cuboidal (square-like, as wide as it is tall), or columnar (rectangular, taller than it is wide). Additionally, the nucleus shape helps identify the type of epithelial cells. Squamous cells have flattened disc-shaped nuclei, cuboidal cells have spherical nuclei, and columnar cells have elongated nuclei.
Based on the number of cell layers,...
Uterus and Cervix01:18

Uterus and Cervix

The uterus, commonly called the womb, is a vital reproductive organ in females designed to provide a nurturing environment for the implantation and growth of an embryo. It is shaped like a hollow pear and positioned between the urinary bladder and the rectum. The uterus's structure allows it to support and protect a developing fetus throughout pregnancy.
The uterus is securely anchored within the pelvic cavity by paired broad ligaments on either side. It is further stabilized by three pairs of...

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Early Dengue Prediction in Bangladesh: A Comparative Study With Feature Analysis, Explainable Artificial Intelligence, and Model Optimization.

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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宫Xpert:一个多结构的卷积神经网络,用于预测宫类型和宫细胞异常.

Rashik Shahriar Akash1, Radiful Islam1, Sm Saiful Islam Badhon2

  • 1Department of Computer Science and Engineering, Daffodil International University, Dhaka, Bangladesh.

Digital health
|November 12, 2024
PubMed
概括

新的人工智能工具CerviXpert精确检测子宫癌细胞异常,并对子宫类型进行分类. 这种高效的模型为早期宫癌查提供了一个有希望的解决方案,特别是在资源有限的环境中.

关键词:
宫癌是发生在宫癌的原因之一.宫细胞类型的细胞类型计算机辅助诊断 计算机辅助诊断 计算机辅助诊断诊断细胞学诊断细胞学多结构的卷积神经网络.

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科学领域:

  • 医疗成像医学成像
  • 医疗保健中的人工智能
  • 在瘤学瘤学.

背景情况:

  • 宫癌是全球主要的健康问题,早期检测显著提高了生存率.
  • 目前的诊断方法,如巴氏涂抹和活检,都取决于操作者,容易出现错误.
  • 需要有效和准确的自动化工具来进行宫癌查.

研究的目的:

  • 开发CerviXpert,一种新的多结构卷积神经网络,用于分类宫类型和检测宫细胞异常.
  • 与现有的最先进模型相比,评估CerviXpert的准确性和计算效率.

主要方法:

  • 使用SiPaKMeD数据集开发了一个计算效率高的卷积神经网络CerviXpert.
  • 该模型架构具有简化的设计,具有有限的卷积层,最大聚合和密集层,从头开始训练.
  • 使用五倍交叉验证评估性能,将CerviXpert与ResNet50,VGG16,MobileNetV2和InceptionV3.3进行比较.

主要成果:

  • 在分类宫细胞异常 (正常,异常,良性) 方面,CerviXpert的准确率达到了98.04%,而在五类宫类型分类方面则达到了98.60%.
  • 该模型在准确性和计算需求方面都超过了MobileNetV2和InceptionV3.
  • CerviXpert的准确性与ResNet50和VGG16相当,但计算复杂性和资源使用量显著降低.

结论:

  • CerviXpert为宫癌查和诊断提供了一个有前途,准确和计算可行的解决方案.
  • 其精简的架构适合在资源有限的环境中部署,有可能提高早期检测和宫癌的管理.