Detecting Geckler Classification from Gram Stained Smears Images for Sputum

Kazuki Hashimoto, Ryosuke Iida, Kouich Hirata, Kimiko Matsuoka, Shigeki Yokoyama

Abstract

A Geckler classification is a criterion how the smear image is quality based on the number of buccal squamous epithelial (BSE) cells and leukocytes in the Gram stained smears images per 100× field for sputum. The Geckler classification then determines which of images is valuable to microscope testing for the Gram stained smears images per 1,000× field for sputum. In this paper, we develop the system to detect the Geckler classification from Gram stained smears images per 100× field for sputum. In this system, first we detect the regions of BSE cells and leukocytes and then construct the classifier of the BSE cells and leukocytes by SVM and DNN. Then, we detect the Geckler class of every test image by detecting the candidate regions and by applying the classifier.

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Paper Citation


in Harvard Style

Hashimoto K., Iida R., Hirata K., Matsuoka K. and Yokoyama S. (2020). Detecting Geckler Classification from Gram Stained Smears Images for Sputum.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 469-476. DOI: 10.5220/0008962304690476


in Bibtex Style

@conference{icpram20,
author={Kazuki Hashimoto and Ryosuke Iida and Kouich Hirata and Kimiko Matsuoka and Shigeki Yokoyama},
title={Detecting Geckler Classification from Gram Stained Smears Images for Sputum},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={469-476},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008962304690476},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Detecting Geckler Classification from Gram Stained Smears Images for Sputum
SN - 978-989-758-397-1
AU - Hashimoto K.
AU - Iida R.
AU - Hirata K.
AU - Matsuoka K.
AU - Yokoyama S.
PY - 2020
SP - 469
EP - 476
DO - 10.5220/0008962304690476