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CystNet:一个人工智能驱动的模型用于PCOS检测,使用超声波图像的多级值

Poonam Moral¹, Debjani Mustafi², Abhijit Mustafi²

¹Department of Computer Science and Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India. phdcs10051.21@bitmesra.ac.in.

Scientific reports

|October 24, 2024

概括

这项研究引入了一个由人工智能驱动的系统,可以从超声波图像中自动检测多囊卵巢综合征 (PCOS),从而提高生殖年龄女性的诊断速度和准确性.

科学领域:

内分泌学和医学成像学
医疗保健中的人工智能

背景情况:

多囊卵巢综合征 (PCOS) 是一种常见的内分泌疾病,影响生殖年龄的女性,其特点是雌激素过多和卵巢卵泡异常.
目前PCOS的诊断方法依赖于手动超声波图像解释,耗时且容易出现错误.
需要自动化系统来简化PCOS诊断并提高准确性.

研究的目的:

开发和评估一个先进的自动化系统来检测和分类多囊卵巢综合征 (PCOS) 使用超声波图像.
通过人工智能驱动的卵巢卵泡特征分析来提高诊断准确性和效率.

主要方法:

图像预处理技术包括调整尺寸,规范化,增大,分水和多级值,以实现精确的细分.
使用拟议的CystNet技术进行特征提取.
通过五倍交叉验证和整体机器学习分类器的完全连接层进行PCOS分类.

主要成果:

人工智能系统使用具有5倍交叉验证的完全连接分类层实现了[公式:参见文本]的令人称赞的准确性.
在使用整体机器学习分类器时,获得了[公式:参见文本]的准确性.
该系统在各种评估指标上表现出强的表现,包括AUC得分,精度,回忆和F1得分.

结论:

拟议的基于人工智能的自动化系统可以有效地从超声波图像中检测和分类PCOS,提供更准确,更有效的诊断工具.
这种方法有可能使用多式联络数据集预测PCOS和类似疾病.
该系统可以促进及时干预,并减少医疗保健专业人员的诊断负担.

关键词:

卷积式自动编码器卷积式自动编码器有效的网络有效的网络毛囊 (Follicles) 是一个毛囊.图像的分类图像的分类.图像细分图像细分图像细分多囊性卵巢综合征多囊性卵巢综合征

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