Description

Malaria is a deadly, infectious and life-threatening mosquito-borne blood disease caused by Plasmodium parasites. Parasite-infected red blood cells under the microscope by qualified technicians. Malaria is a serious and sometimes fatal disease caused by a parasite that commonly infects a certain type of mosquito which feeds on humans. People who get malaria are typically very sick with high fevers, shaking chills, and flu-like illness. Malaria is the deadliest disease in the earth and big hectic work for the health department. The traditional way of diagnosing malaria is by schematic examining blood smears of human beings for parasite-infected red blood cells under the microscope by lab or qualified technicians. The main aim of using image processing is that our model can detect cells from multiple images taken from microscope via thin blood smear and detect them as positive and negative human blood cell and also it performs classification on human blood cell by using deep learning. With the recent advances in deep learning algorithms such as CNN and Extreme Learning Machine (ELM) have been successfully used for malaria disease analysis. Finally, the system can estimate some performance metrics such as accuracy and error for both algorithms and compare the results in the form of graph.