• Miss. Vaishnavi A. Takey

Malaria is responsible for nearly 438,000 deaths worldwide in a year. A total of 214 million cases of malaria are encountered annually. The conventional method for testing malaria is through microscopy. A blood sample of the patient is spread over a glass slide, stained with Giemsa stain and examined under a microscope. It takes a few hours and a highly trained professional to visually examine the slide and give the results. It is even more difficult to detect the different types of malaria parasite and their stages by the conventional methods. In this work an image processing system was developed to identify malaria parasites in thin blood smears and to classify them into one of the four species of malaria. Many techniques were implemented in the preprocessing stage to enhance the images. In the first part of the system morphological processing is applied to extract the Red Blood Cells (RBC) from blood images. The developed algorithm picks the suspicious regions and detects the parasites in the images including the overlapped cells. Accordingly, the RBCs are classified into infected and non-infected cells and the number of RBCs in each image is calculated. The aim of this paper is to review and analyze the recent work of different researchers in the area of malaria parasite. This paper provides a good basis for researchers who are starting to investigate the computer aided malaria diagnosis methods.

Malaria parasite, Plasmodium, Feature Extraction, Classification, Malaria diagnosis

"“A SURVEY ON DETECTION AND CLASSIFICATION OF PLASMODIUM PARASITES USING MACHINE LEARNING TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.5, Issue 11, page no.584-586, November-2018, Available :http://www.jetir.org/papers/JETIR1811588.pdf

"“A SURVEY ON DETECTION AND CLASSIFICATION OF PLASMODIUM PARASITES USING MACHINE LEARNING TECHNIQUE", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.5, Issue 11, page no. pp584-586, November-2018, Available at : http://www.jetir.org/papers/JETIR1811588.pdf