论文标题
疟疾检测和分类
Malaria Detection and Classificaiton
论文作者
论文摘要
根据世界卫生组织的说法，疟疾是一种全球关注的疾病。当今世界上数十亿人面临疟疾的风险。显微镜被认为是疟疾诊断的黄金标准。对血液样本的微观评估要求需要训练有素的专业人员，这些专业人员有时在疟疾问题的农村地区不可用。疟疾诊断的完全自动化是一项具有挑战性的任务。在这项工作中，我们提出了一个诊断疟疾的框架。我们采用两层方法，在第一层中使用更快的RCNN检测受感染的细胞，将其裁剪，然后将裁切的细胞喂入分开的神经网络进行分类。提出的方法已在公开可用的数据集上进行了测试，这将是未来方法的基准，因为目前尚无通用数据集，其中报告了疟疾诊断的结果。
Malaria is a disease of global concern according to the World Health Organization. Billions of people in the world are at risk of Malaria today. Microscopy is considered the gold standard for Malaria diagnosis. Microscopic assessment of blood samples requires the need of trained professionals who at times are not available in rural areas where Malaria is a problem. Full automation of Malaria diagnosis is a challenging task. In this work, we put forward a framework for diagnosis of malaria. We adopt a two layer approach, where we detect infected cells using a Faster-RCNN in the first layer, crop them out, and feed the cropped cells to a seperate neural network for classification. The proposed methodology was tested on an openly available dataset, this will serve as a baseline for the future methods as currently there is no common dataset on which results are reported for Malaria Diagnosis.
