A Review of Conventional and Machine Learning Techniques for Malaria Parasite Detection Using a Thick Blood Smear
Keywords:
Computer-aided diagnosis, CNN, Deep learning, SVM, Malaria detection.Abstract
Life-threatening malaria is caused by parasites that are lethally effective and harmful and are transmitted through the bite of female Anopheles mosquitoes. In 2015, WHO reported more than 200 million deaths occurred because of this. This makes malaria one of the most vulnerable diseases. The Plasmodium parasite needs to be detected at the early stages for the patient’s survival. Microscopists over the years have been made such craftsmen that they through their expertise have been able to diagnose malaria, being followed by an area expansion support from computer-aided diagnosis. But the expertise required for feature extraction were questionable, which were later replaced by deep learning techniques through automatic feature extraction in CNN's. This paper provides a review of some such techniques and methods which were used for the said purposes.
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