TY - GEN
T1 - Plasmodium vivax classification from digitalization microscopic thick blood film using combination of second order statistical feature extraction and K-Nearest Neighbor (K-NN) classifier method
AU - Rahmanti, Farah Zakiyah
AU - Sutojo,
AU - Ningrum, Novita Kurnia
AU - Imania, Niken Kartika
AU - Purnomo, Mauridhi Hery
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2016/2/8
Y1 - 2016/2/8
N2 - Malaria disease is one of the most serious public health problem in various tropical countries, included in Indonesia. Data of The Ministry of Health mentioned that Papua, West Papua, and NTT (Nusa Tenggara Timur) are provinces which have the greatest cases of malaria. With high mortality rate, malaria need to be treated as quick as possible. Therefore, the accurate and timely diagnosis of malaria infection is essential to control and to cure the disease. We propose an accurate method to classify plasmodium vivax from digitalization microscopic thick blood film using combination of second order statistic feature extraction and K-Nearest Neighbor (K-NN) classifier method. In this feature extraction, we use GLCM (Gray Level Co-occurrence Matrix) to get contrasts, correlations, energys, and homogeneity values. Those values will be inserted in classification module as an input. We use K-NN classifier method to classify the red blood film are infected by plasmodium vivax or not. This process can also classify plasmodium vivax into thropozoit, schizont, and gametocytes. Based on the result of experiments, the combination of second order statistical and K-NN has a high accuracy for classifying plasmodium vivax with average accuracy 95%.
AB - Malaria disease is one of the most serious public health problem in various tropical countries, included in Indonesia. Data of The Ministry of Health mentioned that Papua, West Papua, and NTT (Nusa Tenggara Timur) are provinces which have the greatest cases of malaria. With high mortality rate, malaria need to be treated as quick as possible. Therefore, the accurate and timely diagnosis of malaria infection is essential to control and to cure the disease. We propose an accurate method to classify plasmodium vivax from digitalization microscopic thick blood film using combination of second order statistic feature extraction and K-Nearest Neighbor (K-NN) classifier method. In this feature extraction, we use GLCM (Gray Level Co-occurrence Matrix) to get contrasts, correlations, energys, and homogeneity values. Those values will be inserted in classification module as an input. We use K-NN classifier method to classify the red blood film are infected by plasmodium vivax or not. This process can also classify plasmodium vivax into thropozoit, schizont, and gametocytes. Based on the result of experiments, the combination of second order statistical and K-NN has a high accuracy for classifying plasmodium vivax with average accuracy 95%.
KW - K-Nearest Neighbor
KW - Malaria
KW - Plasmodium Vivax
KW - Second Order Statistical
KW - Thick Blood Film
UR - http://www.scopus.com/inward/record.url?scp=84963960850&partnerID=8YFLogxK
U2 - 10.1109/ICICI-BME.2015.7401339
DO - 10.1109/ICICI-BME.2015.7401339
M3 - Conference contribution
AN - SCOPUS:84963960850
T3 - Proceedings - 2015 4th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering, ICICI-BME 2015
SP - 79
EP - 83
BT - Proceedings - 2015 4th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering, ICICI-BME 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Instrumentation, Communications, Information Technology and Biomedical Engineering, ICICI-BME 2015
Y2 - 2 November 2015 through 3 November 2015
ER -
