Embedded deep-learning based sample-to-answer device for on-site malaria diagnosis

Improvements in digital microscopy are critical for the development of a malaria diagnosis method that is accurate at the cellular level and exhibits satisfactory clinical performance. Digital microscopy can be enhanced by improving deep learning algorithms and achieving consistent staining results. In this study, a novel miLabTM device incorporating the... Show more