智能诊断模型用于疟疾寄生虫检测和分类,使用基于强制性开始的囊神经网络
Golla Madhu1, Ali Wagdy Mohamed2,3, Sandeep Kautish4
1Department of Information Technology, VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India.
Scientific reports
|August 17, 2023
概括
这项研究介绍了一种使用囊网络的AI系统,用于从血液细胞图像中更快,更准确地诊断疟疾. 自动化方法在人工显微镜上有所改进,有助于及时有效地治疗疟疾.
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
- 寄生虫学的寄生虫学
背景情况:
- 疟疾是由虫寄生虫传播的疟原虫引起的,是一种严重的疾病,需要及时治疗.
- 目前的疟疾诊断依赖于血液涂抹的手动显微镜,这是耗时的,主观的,需要专家人员.
- 需要自动化,准确和高效的疟疾诊断系统,对于有效的疾病管理至关重要.
研究的目的:
- 开发一种创新的自动化系统用于疟疾诊断,使用基于初始的囊网络.
- 在显微镜图像中精确区分寄生和未感染的细胞,用于疟疾检测.
- 为疟疾诊断提供一种更有效,更可靠的替代传统手动显微镜.
主要方法:
- 使用基于启动的囊网络架构进行图像分析.
- 使用Inception V3从疟疾细胞图像中提取特征,从而实现高效的表示学习.
- 实现了一个动态命令性囊神经网络,用于将细胞分类为寄生虫或健康.
主要成果:
- 拟议的系统在疟疾寄生虫识别准确度方面取得了显著的改善.
- 与传统的手动显微镜方法相比,实现了更高的准确性和速度.
- 成功地将显微镜图像分为寄生虫和健康细胞,使寄生虫检测成为可能.
结论:
- 开发的AI系统为疟疾诊断提供了强大而高效的解决方案.
- 利用像囊网络这样的最先进技术可以显著提高疟疾检测能力.
- 这种自动化方法支持及时和准确的治疗,这对于打击疟疾至关重要.
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