Automating malaria diagnosis: a machine learning approach: Erythrocyte segmentation and parasite identification in thin blood smear microscopy images using convolutional neural networks

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained...

Kalman filtering approaches to enhance scanning speed and precision in automated microscopy

Fast and accurate diagnosis of illnesses or other health complications is not accessible in many locations around the world. Due to this, illnesses are unnecessarily left untreated. Therefore, AiDx Medical has developed a portable automated diagnostic microscope with re liable and rapid AI-assisted detection specifically for low-resource...

Automating the Diagnosis and Quantification of Urinary Schistosomiasis

Schistosomiasis is a neglected tropical disease (NTD) that affects around a quarter-billion people worldwide. Most of the infected people live in tropical regions, especially in sub-Saharan Africa, where there is limited access to diagnostic and other relevant medical facilities. The current reference standard diagnostic procedure - conventional...