Malaria detection using convolutional neural network

Abstract

In the tropical and subtropical countries, malaria has been a challenge, which really needs a quick and precise diagnosis to stop or control the disease. It is a serious and sometimes fatal disease caused by a parasite that commonly infects a certain type of mosquito which feeds on humans. The traditional microscopy technique has a few weaknesses which incorporate time utilization and reproducibility of the results. This paper deals with the automatic identification of malaria-infected cells using deep learning methods. Deep learning methods have the advantage of being able to automatically learn the features from the input data, thereby requiring minimal inputs from human experts for automated malaria diagnosis. An automated diagnostic system can significantly improve the efficiency of the pathologists and also reduce the need for dedicated pathologists in rural villages.