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

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

17 Atıf (Scopus)

Özet

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.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	UBMK 2018 - 3rd International Conference on Computer Science and Engineering
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
Sayfalar	298-302
Sayfa sayısı	5
ISBN (Elektronik)	9781538678930
DOI'lar	https://doi.org/10.1109/UBMK.2018.8566549
Yayın durumu	Yayınlandı - 6 Ara 2018
Harici olarak yayınlandı	Evet
Etkinlik	3rd International Conference on Computer Science and Engineering, UBMK 2018 - Sarajevo, Bosnia and Herzegovina Süre: 20 Eyl 2018 → 23 Eyl 2018

Yayın serisi

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

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	3rd International Conference on Computer Science and Engineering, UBMK 2018
Ülke/Bölge	Bosnia and Herzegovina
Şehir	Sarajevo
Periyot	20/09/18 → 23/09/18

Bibliyografik not

Publisher Copyright:
© 2018 IEEE.

BM SKH

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Belgeye Erişim

10.1109/UBMK.2018.8566549

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

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). Makale 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

@inproceedings{758686bb9e0a4879a27e5ea02959614f,

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 = "Convolutional neural network, computer-aided diagnosis, 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",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "298--302",

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

address = "United States",

}

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).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Malaria Parasite Detection with Deep Transfer Learning

AU - Var, Esra

AU - Boray Tek, F.

PY - 2018/12/6

Y1 - 2018/12/6

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 - Convolutional neural network

KW - computer-aided diagnosis

KW - deep learning

KW - malaria

UR - https://www.scopus.com/pages/publications/85060661093

U2 - 10.1109/UBMK.2018.8566549

DO - 10.1109/UBMK.2018.8566549

M3 - Conference contribution

AN - SCOPUS:85060661093

T3 - UBMK 2018 - 3rd International Conference on Computer Science and Engineering

SP - 298

EP - 302

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

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd International Conference on Computer Science and Engineering, UBMK 2018

Y2 - 20 September 2018 through 23 September 2018

ER -

Malaria Parasite Detection with Deep Transfer Learning

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BM SKH

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