Deep Learning Based Annotation of Datasets for Malaria Diagnosis

According to UNICEF, malaria kills a child every 2 minutes, especially in marginalised communities. Malaria mortality can be drastically reduced by ensuring prompt access to diagnosis and treatment. However, using the microscope, the recommended diagnosis tool, is expensive, expert dependent, time-consuming for a single diagnosis and becomes impractical in areas with a high disease burden. Traditional data labeling processes use tools such as Labelmg to manually annotate every object and this makes it an expensive process and time consuming thus leading to limited dataset especially in a marginalized community. In this research, we present findings on using deep learning techniques to facilitate a fast and effective creation of ground truth datasets to be used in developing relevant malaria diagnosis tools, drawing on data from Tanzania. Our results demonstrate that it took one third less time with high efficiency to annotate the dataset compared to traditional methods. This annotation technique provides the assurance of the availability of high-quality labeled malaria datasets that can be used to develop machine learning based malaria diagnosis tools.