The Implementation of Deep Learning for White Blood Cell Subtype Classification from Microscopic Images

Yustisia Amalia; Miftahul Khairoh; Arkha B.; Budi Santoso

Proceedings of the 1st Asian Conference on Humanities, Industry, and Technology for Society, ACHITS 2019, 30-31 July 2019, Surabaya, Indonesia

Research Article

The Implementation of Deep Learning for White Blood Cell Subtype Classification from Microscopic Images

Download530 downloads

Cite: BibTeX Plain Text

@INPROCEEDINGS{10.4108/eai.30-7-2019.2287610,
    author={Yustisia Amalia and Miftahul Khairoh and Arkha Rosyaria B. and Budi Santoso},
    title={The Implementation of Deep Learning for White Blood Cell Subtype Classification from Microscopic Images},
    proceedings={Proceedings of the 1st Asian Conference on Humanities, Industry, and Technology for Society, ACHITS 2019, 30-31 July 2019, Surabaya, Indonesia},
    publisher={EAI},
    proceedings_a={ACHITS},
    year={2019},
    month={9},
    keywords={deep learning white blood cell classification},
    doi={10.4108/eai.30-7-2019.2287610}
}

Yustisia Amalia
Miftahul Khairoh
Arkha Rosyaria B.
Budi Santoso
Year: 2019
The Implementation of Deep Learning for White Blood Cell Subtype Classification from Microscopic Images
ACHITS
EAI
DOI: 10.4108/eai.30-7-2019.2287610

Yustisia Amalia¹^,*, Miftahul Khairoh¹, Arkha Rosyaria B.¹, Budi Santoso²

1: Health Science Faculty, Indonesia
2: Engineering Faculty, Dr.Soetomo University Surabaya, Indonesia

*Contact email: yusti.amalia@unitomo.ac.id

Abstract

This study aims to apply one of the artificial intelligence algorithms namely deep learning in the classification process of white blood cell subtypes. Classification of white blood cell subtypes is needed for identification of blood diseases such as leukemia, anemia, etc. The deep learning model used in this study is the LeNet architecture which is easy to implement. After going through the preprocessing of white blood cell microscopic images and classification of datasets according to the method, the classification accuracy was 94%..

Keywords: deep learning white blood cell classification

Published: 2019-09-30
Publisher: EAI

: http://dx.doi.org/10.4108/eai.30-7-2019.2287610