TY - GEN
T1 - Malaria parasite identification on thick blood film using genetic programming
AU - Purnama, I. Ketut Eddy
AU - Rahmanti, Farah Zakiyah
AU - Purnomo, Mauridhi Hery
PY - 2013
Y1 - 2013
N2 - 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 color 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 classiffied into two classes. The classification has an average accuracy of 95.49% for non-parasites and 95.58% for parasites. Meanwhile when system is used to classified into six classes, testing result have an average accuracy of 90.25% not parasites, 82.25% vivax thropozoit, 75.83% vivax schizont, 81.75% vivax gametocytes, 90.75% falciparum thropozoit, 86.75% falciparum gametocytes. This research confirm that identifying malaria parasite in thick blood film is possible.
AB - 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 color 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 classiffied into two classes. The classification has an average accuracy of 95.49% for non-parasites and 95.58% for parasites. Meanwhile when system is used to classified into six classes, testing result have an average accuracy of 90.25% not parasites, 82.25% vivax thropozoit, 75.83% vivax schizont, 81.75% vivax gametocytes, 90.75% falciparum thropozoit, 86.75% falciparum gametocytes. This research confirm that identifying malaria parasite in thick blood film is possible.
KW - Feature Extraction
KW - Genetic Programming (GP)
KW - Malaria Parasite
KW - Receiver Operating Characteristics (ROC)
KW - Thick Blood Film
UR - http://www.scopus.com/inward/record.url?scp=84893621857&partnerID=8YFLogxK
U2 - 10.1109/ICICI-BME.2013.6698491
DO - 10.1109/ICICI-BME.2013.6698491
M3 - Conference contribution
AN - SCOPUS:84893621857
SN - 9781479916504
T3 - Proc. of 2013 3rd Int. Conf. on Instrumentation, Communications, Information Technol., and Biomedical Engineering: Science and Technol. for Improvement of Health, Safety, and Environ., ICICI-BME 2013
SP - 194
EP - 198
BT - Proc. of 2013 3rd Int. Conf. on Instrumentation, Communications, Information Technol., and Biomedical Engineering
PB - IEEE Computer Society
T2 - 2013 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2013
Y2 - 7 November 2013 through 8 November 2013
ER -
