Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image

Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determi...

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Main Authors: Nur Khomairoh, Riyanto Sigit, Tri Harsono, Yetti Hernaningsih, Anwar
Format: Book Section PeerReviewed
Language:English
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Published: IEEE 2020
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Online Access:http://repository.unair.ac.id/106370/1/Segmentation.pdf
http://repository.unair.ac.id/106370/2/Bukti%20Etik%20Segmentation.pdf
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https://doi.org/10.1109/IES50839.2020.9231651
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spelling id-langga.1063702021-05-05T03:19:26Z http://repository.unair.ac.id/106370/ Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image Nur Khomairoh Riyanto Sigit Tri Harsono Yetti Hernaningsih Anwar R Medicine (General) RB37-56.5 Clinical pathology. Laboratory technique Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively. IEEE 2020 Book Section PeerReviewed text en http://repository.unair.ac.id/106370/1/Segmentation.pdf text en http://repository.unair.ac.id/106370/2/Bukti%20Etik%20Segmentation.pdf text en http://repository.unair.ac.id/106370/3/Segmentation%20System%20of%20Acute%20Myeloid%20Leukemia%20%28AML%29%20Subtypes%20on%20Microscopic%20Blood%20Smear%20Image.pdf text en http://repository.unair.ac.id/106370/4/Segmentation%20System%20of%20Acute%20Myeloid%20Leukemia%20%28AML%29%20Subtypes%20on%20Microscopic%20Blood%20Smear%20Image.pdf Nur Khomairoh and Riyanto Sigit and Tri Harsono and Yetti Hernaningsih and Anwar (2020) Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image. In: 2020 International Electronics Symposium (IES). IEEE, pp. 1-6. https://ieeexplore.ieee.org/document/9231651/keywords#keywords https://doi.org/10.1109/IES50839.2020.9231651
institution Universitas Airlangga
building Universitas Airlangga Library
continent Asia
country Indonesia
Indonesia
content_provider Universitas Airlangga Library
collection UNAIR Repository
language English
English
English
English
topic R Medicine (General)
RB37-56.5 Clinical pathology. Laboratory technique
spellingShingle R Medicine (General)
RB37-56.5 Clinical pathology. Laboratory technique
Nur Khomairoh
Riyanto Sigit
Tri Harsono
Yetti Hernaningsih
Anwar
Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image
description Leukemia is a blood cancer that attacks human white blood cells. This disease is divided into four types, including Acute Myeloid Leukemia (AML). AML is the most common type of acute leukemia, and it has eight types of subtypes distinguished by the level of cell maturation. Medical personnel determines the type of AML based on microscopic images of blood cell smears that contain white blood cells, red blood cells, and pieces of blood. This research builds a segmentation system that can determine the boundary of an object with the surrounding area, where the object sought is white blood cells contained in microscopic images of blood cell smears. White blood cells are sought based on ROI using the Haar Cascade Classifier, and then segmentation is carried out on the nucleus and cytoplasm. AML sub-types used as objects in this study are M4, M5, and M7. Based on the results of experimental data on the segmentation system, the nucleus segmentation in each cell of M4, M5, and M7 with an accuracy of 87.5%, 90.4%, 84.6% in sequence, and the results of cytoplasm segmentation are 75%, 71.4%, and 80.76%, respectively.
format Book Section
PeerReviewed
author Nur Khomairoh
Riyanto Sigit
Tri Harsono
Yetti Hernaningsih
Anwar
author_facet Nur Khomairoh
Riyanto Sigit
Tri Harsono
Yetti Hernaningsih
Anwar
author_sort Nur Khomairoh
title Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image
title_short Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image
title_full Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image
title_fullStr Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image
title_full_unstemmed Segmentation System of Acute Myeloid Leukemia (AML) Subtypes on Microscopic Blood Smear Image
title_sort segmentation system of acute myeloid leukemia (aml) subtypes on microscopic blood smear image
publisher IEEE
publishDate 2020
url http://repository.unair.ac.id/106370/1/Segmentation.pdf
http://repository.unair.ac.id/106370/2/Bukti%20Etik%20Segmentation.pdf
http://repository.unair.ac.id/106370/3/Segmentation%20System%20of%20Acute%20Myeloid%20Leukemia%20%28AML%29%20Subtypes%20on%20Microscopic%20Blood%20Smear%20Image.pdf
http://repository.unair.ac.id/106370/4/Segmentation%20System%20of%20Acute%20Myeloid%20Leukemia%20%28AML%29%20Subtypes%20on%20Microscopic%20Blood%20Smear%20Image.pdf
http://repository.unair.ac.id/106370/
https://ieeexplore.ieee.org/document/9231651/keywords#keywords
https://doi.org/10.1109/IES50839.2020.9231651
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