An Extensive Survey on Classification of Malaria Parasites in Patients Based on Fuzzy Approaches

Abstract

Malaria is one of the most potentially dangerous diseases worldwide that is disseminated by the bites of infected mosquitoes. Timely diagnosis and proper treatment are the two indispensable factors in reducing the rate of death and disease transmission. Many approaches and frameworks have already been designed to scrutinize the malaria parasites in the blood images. For better accuracy and convenience, several Fuzzy approaches have been widely used for perceiving the presence of malaria. Some of the most popular automated Fuzzy systems proposed for observing malaria microbes include neuro-fuzzy inference, fuzzy expert system, Type-2 fuzzy-based logic, and mobile-based fuzzy system. Unsupervised color segmentation, edge detection, and object recognition based on fuzzy logic in malaria-infected microscopic blood images are some of the advanced works done to obtain malaria parasites. These computational approaches assist in diagnosing the disease and avoid the time-consuming traditional method of pursuing the disorder. Much work has been done in the literature, but a theoretical survey is missing for conducting substantial research. The present chapter aims to present an extensive study of scrutinizing the presence of malaria in a patient based on observed symptoms or examining blood samples. This survey would help medical practitioners and interdisciplinary researchers to study the summarized state-of-the-art works done in one place.