Malaria Parasite Detection and Prediction using Blood Smear and Symptoms

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Shweta Tembhurnikar, Swati Savkare, Shwetanshu Singh, Pawan Maher


Malaria stays a big burden on international health, with round two hundred million instances worldwide. Malaria is transmitted through the bites of infected female Anopheles mosquitoes which infects the red blood cells of the human body. The paper is proposing a malaria detection and prediction system. In this system, two modules are used, the input is given as the csv dataset and image dataset. In first module, the image datasets are pre-processed, in preprocessing the image is converted from RGB to Gray scale, then segmentation is done. After segmentation, data is extracted to check the feature of that image and then classified with CNN algorithm and it gives result as whether disease identified or not. The second module is based on symptoms analysis, the symptoms of a patient are given as an input. The prediction is done using SVM algorithm. This system can be used in hospital and test labs to predict and detect the malaria.

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