Malaria Parasite Detection Using Deep Learning

Malaria is the world’s worst disease and a substantial financial strain on our healthcare system. For the traditional laboratory diagnosis of malaria, distinguishing between healthy and infected red blood cells (RBCs) necessitates the presence of a skilled individual and careful observation. It is very inefficient and prone to human errors. The purpose of this study is to implement a detection system which can detect malaria using blood smear images. Our custom deep learning model detects whether it is infected with malaria parasite or not. The findings demonstrate that the automation of the process is capable of accurately detecting the malaria parasite in blood samples with an accuracy exceeding 98% while presenting a lower level of complexity compared to previous approaches documented in the literature.