Malaria Parasite Detection with Deep Transfer Learning

Esra Var; F. Boray Tek

doi:10.1109/UBMK.2018.8566549

Malaria Parasite Detection with Deep Transfer Learning

Esra Var, F. Boray Tek

Isik University

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

16 Citations (Scopus)

Abstract

This study aims to aromatically detect malaria parasites (Plasmodium sp) on images taken from Giemsa stained blood smears. Deep learning methods provide limited performance when sample size is low. In transfer learning, visual features are learned from large general data sets, and problem-specific classification problem can be solved successfully in restricted problem specific data sets. In this study, we apply transfer learning method to detect and classify malaria parasites. We use a popular pre-trained CNN model VGG19. We trained the model for 20 epoch on 1428 P. Vivax, 1425 P. Ovale, 1446 P. Falciparum, 1450 P. Malariae and 1440 non-parasite samples. The transfer learning model achieves %80, %83, %86, %75 precision and 83%, 86%, 86%, 79% f-measure on 19 test images.

Original language	English
Title of host publication	UBMK 2018 - 3rd International Conference on Computer Science and Engineering
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	298-302
Number of pages	5
ISBN (Electronic)	9781538678930
DOIs	https://doi.org/10.1109/UBMK.2018.8566549
Publication status	Published - 6 Dec 2018
Externally published	Yes
Event	3rd International Conference on Computer Science and Engineering, UBMK 2018 - Sarajevo, Bosnia and Herzegovina Duration: 20 Sept 2018 → 23 Sept 2018

Publication series

Name	UBMK 2018 - 3rd International Conference on Computer Science and Engineering

Conference

Conference	3rd International Conference on Computer Science and Engineering, UBMK 2018
Country/Territory	Bosnia and Herzegovina
City	Sarajevo
Period	20/09/18 → 23/09/18

Bibliographical note

Publisher Copyright:
© 2018 IEEE.

Keywords

computer-aided diagnosis
Convolutional neural network
deep learning
malaria

UN SDGs

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

Access to Document

10.1109/UBMK.2018.8566549

Cite this

Var, E., & Boray Tek, F. (2018). Malaria Parasite Detection with Deep Transfer Learning. In UBMK 2018 - 3rd International Conference on Computer Science and Engineering (pp. 298-302). Article 8566549 (UBMK 2018 - 3rd International Conference on Computer Science and Engineering). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/UBMK.2018.8566549

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title = "Malaria Parasite Detection with Deep Transfer Learning",

abstract = "This study aims to aromatically detect malaria parasites (Plasmodium sp) on images taken from Giemsa stained blood smears. Deep learning methods provide limited performance when sample size is low. In transfer learning, visual features are learned from large general data sets, and problem-specific classification problem can be solved successfully in restricted problem specific data sets. In this study, we apply transfer learning method to detect and classify malaria parasites. We use a popular pre-trained CNN model VGG19. We trained the model for 20 epoch on 1428 P. Vivax, 1425 P. Ovale, 1446 P. Falciparum, 1450 P. Malariae and 1440 non-parasite samples. The transfer learning model achieves %80, %83, %86, %75 precision and 83%, 86%, 86%, 79% f-measure on 19 test images.",

keywords = "computer-aided diagnosis, Convolutional neural network, deep learning, malaria",

author = "Esra Var and {Boray Tek}, F.",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 3rd International Conference on Computer Science and Engineering, UBMK 2018 ; Conference date: 20-09-2018 Through 23-09-2018",

year = "2018",

month = dec,

day = "6",

doi = "10.1109/UBMK.2018.8566549",

language = "English",

series = "UBMK 2018 - 3rd International Conference on Computer Science and Engineering",

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Var, E & Boray Tek, F 2018, Malaria Parasite Detection with Deep Transfer Learning. in UBMK 2018 - 3rd International Conference on Computer Science and Engineering., 8566549, UBMK 2018 - 3rd International Conference on Computer Science and Engineering, Institute of Electrical and Electronics Engineers Inc., pp. 298-302, 3rd International Conference on Computer Science and Engineering, UBMK 2018, Sarajevo, Bosnia and Herzegovina, 20/09/18. https://doi.org/10.1109/UBMK.2018.8566549

Malaria Parasite Detection with Deep Transfer Learning. / Var, Esra; Boray Tek, F.
UBMK 2018 - 3rd International Conference on Computer Science and Engineering. Institute of Electrical and Electronics Engineers Inc., 2018. p. 298-302 8566549 (UBMK 2018 - 3rd International Conference on Computer Science and Engineering).

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

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AU - Boray Tek, F.

PY - 2018/12/6

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N2 - This study aims to aromatically detect malaria parasites (Plasmodium sp) on images taken from Giemsa stained blood smears. Deep learning methods provide limited performance when sample size is low. In transfer learning, visual features are learned from large general data sets, and problem-specific classification problem can be solved successfully in restricted problem specific data sets. In this study, we apply transfer learning method to detect and classify malaria parasites. We use a popular pre-trained CNN model VGG19. We trained the model for 20 epoch on 1428 P. Vivax, 1425 P. Ovale, 1446 P. Falciparum, 1450 P. Malariae and 1440 non-parasite samples. The transfer learning model achieves %80, %83, %86, %75 precision and 83%, 86%, 86%, 79% f-measure on 19 test images.

AB - This study aims to aromatically detect malaria parasites (Plasmodium sp) on images taken from Giemsa stained blood smears. Deep learning methods provide limited performance when sample size is low. In transfer learning, visual features are learned from large general data sets, and problem-specific classification problem can be solved successfully in restricted problem specific data sets. In this study, we apply transfer learning method to detect and classify malaria parasites. We use a popular pre-trained CNN model VGG19. We trained the model for 20 epoch on 1428 P. Vivax, 1425 P. Ovale, 1446 P. Falciparum, 1450 P. Malariae and 1440 non-parasite samples. The transfer learning model achieves %80, %83, %86, %75 precision and 83%, 86%, 86%, 79% f-measure on 19 test images.

KW - computer-aided diagnosis

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Malaria Parasite Detection with Deep Transfer Learning

Abstract

Publication series

Conference

Bibliographical note

Keywords

UN SDGs

Access to Document

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