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Detection Of Malaria Parasite Using Deep Learning

EasyChair Preprint no. 2540

4 pagesDate: February 4, 2020


As technology has evolved it has become more and more efficient to diagnose, and treat multiple diseases. Malaria is one of the deadliest diseases on this planet. Each year it estimated that 1 million people die as a result of this disease. Furthermore 3.4 billion people are in danger of contracting malaria. With advances in the field of medicine it is now entirely possible to not only treat but also prevent malaria. The way in which people are diagnosed for malaria today is through blood samples. The techniques used currently are accurate however they are time consuming. This has necessitated doctors to start the treatment for malaria before the blood work is finished, since in its later stage’s malaria can be very difficult to cure. The system that is discussed aims to cut this time requirement by at least half and increase the accuracy of the tests. An automated system that gathers the image data and analyses the images for malarial parasites is described. A system for collection of data and analysis is described. By implementing this system, the time needed for the diagnosis of malaria will be cut down. This will save lives and the medical resources that are used while waiting for the results of the tests from the old system.

Keyphrases: deep learning, Malaria, Parasites

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Harshal Mehta and Saraswati Nagtilak and Sunil Rai and Yashodhan Joglekar and Harshada Thombre and Harsh Mirani},
  title = {Detection Of Malaria Parasite Using Deep Learning},
  howpublished = {EasyChair Preprint no. 2540},

  year = {EasyChair, 2020}}
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