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通过修改的EfficientNetV2-S和残疾妇女和乳腺癌的循环学习率策略,提高改善诊断的可访问性

Moteeb Al Moteri¹, T R Mahesh², Arastu Thakur²

¹Department of Management Information Systems, College of Business Administration, King Saud University, Riyadh, Saudi Arabia.

Frontiers in medicine

|March 22, 2024

概括

一个新的EfficientNetV2机器学习模型从组织病理图像中实现了99.68%的乳腺癌检测准确度,提高了诊断的速度和可靠性.

科学领域:

在瘤学瘤学.
计算机科学计算机科学
医疗成像医学成像

背景情况:

准确及时检测乳腺癌对于有效治疗至关重要.
传统的组织病理学是耗时的,并且受观察者变化的影响.
机器学习为自动化,精确和快速的乳腺癌诊断提供了潜力.

研究的目的:

开发和评估用于自动化乳腺癌图像分类的先进机器学习模型.
解决现有模型的局限性,包括可概括性,性能和可解释性.
为了提高乳腺癌诊断的准确性,效率和稳定性,使用组织病理图像.

主要方法:

使用了EfficientNetV2模型,为乳腺癌图像分类进行了微调.
雇佣了严格的培训和验证BreakHis对基因病学图像的数据集.
实施了先进的数据预处理,增强和循环学习率策略.

主要成果:

在分类组织病理图像方面实现了99.68%的高准确率.
证明了平衡的精度和回忆力,通过显著的F1得分来表示.
报告了相当大的科恩的卡帕值,这意味着强大的诊断性能.

结论:

拟议的EfficientNetV2模型显著提高了乳腺癌检测的可靠性和有效性,与目前的技术相比.
该模型的改进可解释性和可访问性旨在使多样化的患者群体受益,包括老年人和残疾人.
这项研究促进了包容性的医学图像解释和公平获取诊断信息.

关键词:

打破他的数据集有效的NetV2 有效的NetV2 深度学习是一种深度学习.组织病理学图像分类图像分类图像处理是图像处理的过程.有残疾的个人.机器学习是机器学习.医学图像解释解释医学图像解释

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