Malaria is a fatal disease that leads to the death of lakhs of individuals every year. Malaria is caused by a microbe belonging to the Plasmodium group. Five types of these organisms cause this disease in a foreign body. P. falciparum, P. vivax, P. ovale, and P. malariae are the five varieties of parasites that cause malaria. P. Falciparum infected are the ones who are more susceptible to keywords as others are mildly infectious. Malaria is spread by a mosquito species called anopheles it is the same that spreads dengue too. However, this disease could be cured if detected at early stages. Detecting malaria is an extremely challenging aspect considering the morphological aspects of the parasite. Malaria is more prevalent in tropical and subtropical climatic conditions. Owing to the parts of our country the monsoon is the season where we could find increased cases. Malarial parasites enter the human body through mosquito saliva, which in turn is transmitted to the blood. In the body of an organism, it develops in the liver and matures there itself, and starts to reproduce. Generally, malaria symptoms are seen after 10-15 days the parasites intrude on the body. The real challenging part is to check the growth of malaria during the monsoon season in the rural part which faces problems like a lack of doctors, nurses, equipment, testing centers, and so on. The traditional methods of detection are time-consuming and not so accurate. Thus, our project aims to recognize and solve these issues.

Keywords : Image Processing, Convoluted Neural Network (CNN), Deep Learning, Machine Learning.