Malaria Disease Detection Using Deep Learning Technique
In the modern era, people are affected by different types of health issues; one of them is Malaria which may be incipient among all aged people. Malaria is a major disease which may be infected by a female mosquito bite and it is spread from one person to another through mosquitoes. The traditional mechanism to detect the malaria disease is visually to examine the blood smears for identifying red blood cells which are affected by malaria-parasites under the microscope and the appearance of an experienced technician. This method is inefficient due to the absence of lab equipment and the diagnosis is very dependent on the seniority or experience of a person. The main intention of this study is to identify the presence or absence of malaria parasites. To overcome the problems in the traditional malaria detecting system, we develop an automated system to detect malaria parasites. For this implementation, we referred to the Kaggle dataset, which consists of 27,558 images that belong to two classes. In this research work, we use a deep learning algorithm for identifying malaria parasites in blood smears of people.