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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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一个基于机器学习的新型混合系统,使用深度学习技术和元启发算法来对各种医疗数据类型进行分类.

Yezi Ali Kadhim1,2,3, Mehmet Serdar Guzel4, Alok Mishra5,6

  • 1College of Engineering, University of Baghdad, Jadriyah, Baghdad 10071, Iraq.

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概括

本研究介绍了一种混合机器学习方法,将深度学习和元启发算法结合起来,用于精确的医学图像诊断. 这种创新方法在从医学成像数据中对大脑瘤和COVID-19病例进行分类时取得了很高的准确性.

关键词:
在 COVID-19 疫情中,自动编码器自动编码器大脑瘤是个大脑瘤这是分类分类的分类.深度学习是一种深度学习.医疗数据集是一个医疗数据集.的元启发式算法.

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科学领域:

  • 计算机科学在医学中的应用
  • 医疗保健中的人工智能
  • 医学图像分析 医学图像分析

背景情况:

  • 准确及时的疾病诊断对于改善患者的治疗结果至关重要.
  • 计算机科学的进步,特别是机器学习,为医疗应用提供了强大的工具.
  • 现有的分析复杂医疗数据的方法,如MRI和CXR图像,可以得到改进,以获得更高的精度.

研究的目的:

  • 开发和评估一种混合机器学习模型,用于准确分类医疗图像.
  • 将深度学习技术与元启发式算法相结合,用于特征选择和维度减少.
  • 为了验证模型在各种医疗数据集上的性能,包括脑瘤和COVID-19胸部X射线.

主要方法:

  • 使用混合机器学习方法,将深度学习 (卷积神经网络 - CNN,自动编码器) 与元启发算法 (粒子优化 - PSO) 集成在一起.
  • 开发了一个组合网络,使用深度学习提取功能,并通过PSO选择最佳功能,减少数据维度.
  • 将混合模型应用于两个医疗数据集:脑瘤MRI和COVID-19胸部X射线 (CXR).

主要成果:

  • 在两个数据集上取得了高度准确的分类结果,证明了模型的有效性.
  • 使用CNN-PSO-SVM组合,COVID-19数据集的最大准确率为99.76%.
  • 大脑瘤数据集通过Autoencoder-PSO-KNN组合实现了99.51%的最大精度.

结论:

  • 拟议的混合机器学习方法为医学图像分类提供了创新和有效的解决方案.
  • 这种方法在保持性能的同时显著降低了数据的维度,从而导致非常准确的诊断预测.
  • 该模型在不同医学成像任务中的成功突出了其在临床环境中广泛应用的潜力.