Plasmodium Detection using Machine Learning 
Neetha K S1, Ram Rushendranath G2, Sri Vardhan R3, Mani Kumar R4, Pramodh K5
1Neeta K S*, Dept. Of CSE, GITAM University, Bengaluru, India.
2Ram Rushendranath Gulla, Dept. Of CSE, GITAM University, Bengaluru, India.
3Sri Vardhan Rachamallu, Dept Of CSE, GITAM University, Bengaluru, India.
4Mani Kumar Rankireddy, Dept Of CSE, GITAM University, Bengaluru, India.
5Pramodh K, Dept Of CSE, GITAM University, Bengaluru, India.
Manuscript received on April 05, 2020. | Revised Manuscript received on April 17, 2020. | Manuscript published on April 30, 2020. | PP: 289-292 | Volume-9 Issue-4, April 2020. | Retrieval Number: C6002029320/2020©BEIESP | DOI: 10.35940/ijeat.C6002.049420
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Plasmodium is one of India’s biggest public health problems. Early prediction of a malaria epidemic is that the secret to malaria morbidity management, mortality as well as reducing the risk of malaria transmission in the community will benefit politicians, health care providers, medical officers, health ministry and other health organizations to better target medical resources to areas of greatest need. In this project, we acquire data sets from hospital databases, which have the information about the causes of malaria, and the images of cells infected with malaria. We then analyze these data sets and feed them to our machine-learning model. Here we are using contour detection and random forest algorithms for training the model and predicting the output
Keywords: Plasmodium, Miasma, Parasites, Malaria Detection, Image acquisition, RBC count, Stained object detection.