Malaria is usually diagnosed by a microbiologist by examining a small sample of blood smear. Reducing mortality from malaria infection is possible if it is diagnosed early and followed with appropriate treatment. While the WHO has set audacious goals of reducing malaria incidence and mortality rates by 90% in 2030 and eliminating malaria in 35 countries by that time, it still remains a difficult challenge. Computer-assisted diagnostics are on the rise these days as they can be used effectively as a primary test in the absence of or providing assistance to a physician or pathologist. The purpose of this paper is to describe an approach to detecting, localizing and counting parasitic cells in blood sample images towards easing the burden on healthcare workers.
翻译:----
利用深度学习模拟实验室中的疟疾检测
翻译后的摘要:
疟疾通常是通过检查一小部分血涂片来诊断的。通过早期诊断和适当的治疗可以减少疟疾感染的死亡率。虽然世界卫生组织设定了在2030年将疟疾发病率和死亡率降低90%,并在35个国家消除疟疾的雄心壮志,但这仍然是一个具有挑战性的问题。随着计算机辅助诊断的兴起,它们可以在健康工作者缺席或向医生或病理学家提供帮助时有效地作为主要测试使用。本文的目的是描述一种在血样图像中检测、定位和计数寄生细胞的方法,以减轻医疗工作者的负担。