A Review on Computational Methods Based on Machine Learning and Deep Learning Techniques for Malaria Detection

Every year around one million human beings all over the world dies of malaria. It is one of the deadliest mosquito-borne diseases that is caused by the presence of parasites. Physicians are using microscopic analysis of patients' blood to detect malaria parasite presence in early stages, but there are problems with this approach that include the time and accuracy. With the advancement in technologies, the expertise shifted towards automated techniques to detect the presence of malaria in the human blood. This paper explores various dataset collected from relevant sources as well as handcrafted data, use of various pre-processing techniques, and substantial computational methods that were used for classification.