Malaria parasite identification on thick blood film using genetic programming

I. Ketut Eddy Purnama; Farah Zakiyah Rahmanti; Mauridhi Hery Purnomo

doi:10.1109/ICICI-BME.2013.6698491

Malaria parasite identification on thick blood film using genetic programming

I. Ketut Eddy Purnama
, Farah Zakiyah Rahmanti
, Mauridhi Hery Purnomo

Department of Electrical Engineering
Institut Teknologi Sepuluh Nopember

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

28 Citations (Scopus)

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 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.

Original language	English
Title of host publication	Proc. of 2013 3rd Int. Conf. on Instrumentation, Communications, Information Technol., and Biomedical Engineering
Subtitle of host publication	Science and Technol. for Improvement of Health, Safety, and Environ., ICICI-BME 2013
Publisher	IEEE Computer Society
Pages	194-198
Number of pages	5
ISBN (Print)	9781479916504
DOIs	https://doi.org/10.1109/ICICI-BME.2013.6698491
Publication status	Published - 2013
Event	2013 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2013 - Bandung, Indonesia Duration: 7 Nov 2013 → 8 Nov 2013

Publication series

Name	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

Conference

Conference	2013 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2013
Country/Territory	Indonesia
City	Bandung
Period	7/11/13 → 8/11/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Feature Extraction
Genetic Programming (GP)
Malaria Parasite
Receiver Operating Characteristics (ROC)
Thick Blood Film

Access to Document

10.1109/ICICI-BME.2013.6698491

Cite this

Purnama, I. K. E., Rahmanti, F. Z., & Purnomo, M. H. (2013). Malaria parasite identification on thick blood film using genetic programming. In 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 (pp. 194-198). Article 6698491 (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). IEEE Computer Society. https://doi.org/10.1109/ICICI-BME.2013.6698491

Purnama, I. Ketut Eddy ; Rahmanti, Farah Zakiyah ; Purnomo, Mauridhi Hery. / Malaria parasite identification on thick blood film using genetic programming. 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. IEEE Computer Society, 2013. pp. 194-198 (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).

@inproceedings{060a4c92089b4090be032ca7e569428e,

title = "Malaria parasite identification on thick blood film using genetic programming",

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 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.",

keywords = "Feature Extraction, Genetic Programming (GP), Malaria Parasite, Receiver Operating Characteristics (ROC), Thick Blood Film",

author = "Purnama, \{I. Ketut Eddy\} and Rahmanti, \{Farah Zakiyah\} and Purnomo, \{Mauridhi Hery\}",

year = "2013",

doi = "10.1109/ICICI-BME.2013.6698491",

language = "English",

isbn = "9781479916504",

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publisher = "IEEE Computer Society",

pages = "194--198",

booktitle = "Proc. of 2013 3rd Int. Conf. on Instrumentation, Communications, Information Technol., and Biomedical Engineering",

address = "United States",

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Purnama, IKE, Rahmanti, FZ & Purnomo, MH 2013, Malaria parasite identification on thick blood film using genetic programming. in 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., 6698491, 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, IEEE Computer Society, pp. 194-198, 2013 3rd International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2013, Bandung, Indonesia, 7/11/13. https://doi.org/10.1109/ICICI-BME.2013.6698491

Malaria parasite identification on thick blood film using genetic programming. / Purnama, I. Ketut Eddy; Rahmanti, Farah Zakiyah; Purnomo, Mauridhi Hery.
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. IEEE Computer Society, 2013. p. 194-198 6698491 (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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 - https://www.scopus.com/pages/publications/84893621857

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 -

Purnama IKE, Rahmanti FZ, Purnomo MH. Malaria parasite identification on thick blood film using genetic programming. In 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. IEEE Computer Society. 2013. p. 194-198. 6698491. (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). doi: 10.1109/ICICI-BME.2013.6698491

Malaria parasite identification on thick blood film using genetic programming

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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Cite this