Hematological profiling of malaria-induced anemia using deep learning

Abstract

However, malaria-induced anemia (MIA) still persists to be one of the global health challenges with many cases of illness and fatalities mainly in pregnant women and children. Diagnosis of malaria and associated hematologic diseases such as anemia is traditionally carried out through examination of blood smear. However, such techniques require expertise, take long periods, and there are high chances of inter-observer variability. Here, an automatic system based on deep learning for detection of Plasmodium parasite and estimation of anemia is introduced. The proposed system uses convolutional neural network (CNN) branch for image analysis and multi-layer perceptron (MLP) branch for analyzing clinical data, thereby using their combination in multi-label classification. The advantage of such technique is the capability of the model to diagnose complex hematological signs and give probabilistic scores. This system was found to be accurate, precise and reliable.