Malaria Parasites Detection Using Deep Neural Network

Abstract

Malaria is a dreadful infectious disease caused by the bite of female Anopheles mosquito, by the protozoan parasites of the genus Plasmodium. It's an epidemic disease and demands rapid and accurate diagnosis for proper intervention. Microscopic test on the thick and thin blood smear to detect the malaria and counts the infected cells is the gold standard for diagnosis of this disease. An automation process in the form of computer-aided diagnosis is much needed as it plays a vital role in fully or semi-automated diagnosis of diseases based on medical image information. Deep learning has vast ranging applications. This work is to build a convolutional neural network to expertly detect the presence of malaria parasitized cells in the thin blood smear. The authors construct the model as small and computationally efficient to obtain the highest level of accuracy possible.