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Abstract：
Malaria is an infectious disease caused by parasites of the genus Plasmodium spp. It is transmitted to humans by the bite of an infected female Anopheles mosquito. It is the most common disease in resource-poor settings, with 241 million malaria cases reported in 2020 according to the World Health Organization. Optical microscopy examination of blood smears is the gold standard technique for malaria diagnosis; however, it is a time-consuming method and a well-trained microscopist is needed to perform the microbiological diagnosis. New techniques based on digital imaging analysis by deep learning and artificial intelligence methods are a challenging alternative tool for the diagnosis of infectious diseases. In particular, systems based on Convolutional Neural Networks for image detection of the malaria parasites emulate the microscopy visualization of an expert. Microscope automation provides a fast and low-cost diagnosis, requiring less supervision. Smartphones are a suitable option for microscopic diagnosis, allowing image capture and software identification of parasites. In addition, image analysis techniques could be a fast and optimal solution for the diagnosis of malaria, tuberculosis, or Neglected Tropical Diseases in endemic areas with low resources. The implementation of automated diagnosis by using smartphone applications and new digital imaging technologies in low-income areas is a challenge to achieve. Moreover, automating the movement of the microscope slide and image autofocusing of the samples by hardware implementation would systemize the procedure. These new diagnostic tools would join the global effort to fight against pandemic malaria and other infectious and poverty-related diseases.
摘要：
疟疾是一种由疟原虫属寄生虫引起的传染病。它是通过受感染的雌性按蚊叮咬传播给人类的。它是资源匮乏地区最常见的疾病，根据世界卫生组织的数据，2020年报告了2.41亿疟疾病例。血液涂片的光学显微镜检查是疟疾诊断的金标准技术；然而，这是一种耗时的方法，需要训练有素的显微镜来进行微生物学诊断。基于深度学习和人工智能方法的数字成像分析的新技术是诊断传染病的具有挑战性的替代工具。特别是,基于卷积神经网络的疟疾寄生虫图像检测系统模仿专家的显微镜可视化。显微镜自动化提供了一个快速和低成本的诊断，需要更少的监督。智能手机是显微镜诊断的合适选择，允许图像捕获和寄生虫的软件识别。此外,图像分析技术可能是诊断疟疾的快速和最佳解决方案，结核病,或资源匮乏的流行地区被忽视的热带病。通过在低收入地区使用智能手机应用程序和新的数字成像技术来实现自动诊断是一项挑战。此外，通过硬件实现使显微镜载玻片的移动和样品的图像自动聚焦自动化将使程序系统化。这些新的诊断工具将加入全球抗击大流行性疟疾和其他传染病和与贫困有关的疾病的努力。
