Image analysis and machine learning-based malaria assessment system

Malaria is an important and worldwide fatal disease that has been widely reported by the World Health Organization (WHO), it about 219 million cases worldwide, with 435,000 of those mortal. The common malaria diagnosis approach heavily reliant on highly trained experts, who use a microscope to examine samples. Therefore, there need create automated solution for malaria. One main objectives this work design tool could be used diagnose from image blood sample. In paper, we firstly developed graphical user interface help segment red cells infected allow users analyze Secondly, Feed-forward Neural Network (FNN) designed classify into two classes. achieved results show proposed techniques can detect malaria, as 92% accuracy database contains 27,560 benchmark images.