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基于深度学习的模型用于检测微观幻灯片中的Leishmania amastigotes:远程医疗的新方法

Alireza Sadeghi¹, Mahdieh Sadeghi², Mahdi Fakhar³

¹Department of Mechatronics Engineering, Faculty of New Sciences and Technologies, University of Tehran, Tehran, Iran.

BMC infectious diseases

|June 2, 2024

概括

这项研究介绍了LeishFuNet,这是一种用于自动化莱什曼病诊断的深度学习模型. 人工智能系统在微观图像中准确检测Leishmania寄生虫,为传统方法提供更快,更可靠的替代方案.

科学领域:

医学寄生虫学医学寄生虫学
医疗保健中的人工智能
计算生物学计算生物学

背景情况:

莱什曼病是一种致命的寄生虫疾病,具有诊断挑战.
目前基于显微镜的检测方法耗时且容易出现错误.
需要自动诊断来改善患者的治疗结果.

研究的目的:

开发一个深度学习模型来自动诊断莱什曼病.
创建一个人工智能驱动的系统,用于准确检测Leishmania寄生虫.
提供一个快速和用户友好的诊断工具.

主要方法:

开发了LeishFuNet,这是一个用于检测寄生虫的深度学习框架.
雇员使用VGG19,ResNet50,MobileNetV2和DenseNet 169进行转移学习,预先培训COVID-19数据.
在292个显微镜图像和融合预测上微调模型;利用Grad-CAM进行解释性.

主要成果:

实现了高诊断性能:98.95%的准确性,100%的灵敏性,98%的特异性.
展示了强大的精度 (97.91%) 和F1得分 (98.92%).
在ROC曲线下的面积达到99%.

结论:

深度学习为莱什曼病诊断提供了一个精确,快速和经济的替代方案.
莱什FuNet系统显示了替代传统诊断技术的潜力.
自动化AI诊断可以显著改善莱什曼病的检测和管理.

关键词:

人工智能的人工智能是人工智能.深度学习是一种深度学习.图像处理图像处理莱什曼尼亚 (Leishmania) 是一种病.机器学习是机器学习.显微镜图像显微镜图像转移学习转移学习

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相关概念视频

Leishmaniasis

Leishmaniasis

Leishmaniasis is a protozoal disease caused by species of the genus Leishmania and transmitted through the bite of infected female sandflies. The parasite exists in two principal morphological forms during its life cycle. A sandfly acquires intracellular amastigotes from an infected reservoir host, such as a dog. Within the sandfly, these forms differentiate into motile, flagellated promastigotes. During a subsequent blood meal, promastigotes are injected into the human host, where they...