Malaria parasite identification on thick blood film using genetic programming

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  author =       "I. Ketut Eddy Purnama and Farah Zakiyah Rahmanti and 
                 Mauridhi Hery Purnomo",
  booktitle =    "3rd International Conference on Instrumentation,
                 Communications, Information Technology, and Biomedical
                 Engineering (ICICI-BME 2013)",
  title =        "Malaria parasite identification on thick blood film
                 using genetic programming",
  year =         "2013",
  month =        "7-8 " # nov,
  pages =        "194--198",
  address =      "Bandung",
  keywords =     "genetic algorithms, genetic programming, Thick Blood
                 Film, Malaria Parasite, Feature Extraction, Receiver
                 Operating Characteristics, ROC",
  DOI =          "doi:10.1109/ICICI-BME.2013.6698491",
  size =         "5 pages",
  abstract =     "Thin blood film is used to know type and phase of the
                 malaria parasite, but which is widely used in Indonesia
                 is the thick blood film. Therefore we need a method
                 that can identify parasites in thick blood film image
                 with a high percentage of accuracy. This research aims
                 to establish a more objective classification system and
                 reduce the subjective factors of medical personnel in
                 diagnosing the type of malaria parasite include its
                 phase. It has three main stages, there are
                 preprocessing, feature extraction, and classification.
                 Preprocessing aims to eliminate the noise, feature
                 extraction using red-green-blue channel colour
                 histogram, hue channel HSV histogram, and hue channel
                 HSI histogram, classification using Genetic Programming
                 to identify parasites and also to detect type and phase
                 of the parasite. Experiment was conducted on 180 thick
                 blood film images that classified into two classes. The
                 classification has an average accuracy of 95.49percent
                 for non-parasites and 95.58percent for parasites.
                 Meanwhile when system is used to classified into six
                 classes, testing result have an average accuracy of
                 90.25percent not parasites, 82.25percent vivax
                 thropozoit, 75.83percent vivax schizont, 81.75percent
                 vivax gametocytes, 90.75percent falciparum thropozoit,
                 86.75percent falciparum gametocytes. This research
                 confirm that identifying malaria parasite in thick
                 blood film is possible.",
  notes =        "Indonesia, DSLR Camera. 25 features, pop 100,

                 learning the images Does not give evolved formulae.
                 Also known as \cite{6698491}",

Genetic Programming entries for I Ketut Eddy Purnama Farah Zakiyah Rahmanti Mauridhi Hery Purnomo