Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image

Preetham Kumar; Shazad Maneck Udwadia

doi:10.1109/ICACCI.2017.8126106

Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image

Preetham Kumar, Shazad Maneck Udwadia

Department of Information and Communication Technology, Manipal Institute of Technology, Manipal

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

13 Citations (Scopus)

Abstract

Cancer of blood-forming tissues is called Leukemia. This disease hinders the body's ability to fight infection. Leukemia can be categorized into many types. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the two main types. The blood cells' growth and bone marrow are affected by AML. Collection of myeloid blasts in the bone marrow is one of the main characteristics of AML. In this research, a novel method is analyzed to detect the presence of AML. The paper proposes a technique that automatically detects and segments the nucleus from white blood cells (WBCs) in the microscopic blood smear images. Segmentation and clustering is done using a K-Means algorithm, while classification is done using Support Vector Machine (SVM) with feature reduction.

Original language	English
Title of host publication	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1803-1808
Number of pages	6
Volume	2017-January
ISBN (Electronic)	9781509063673
DOIs	https://doi.org/10.1109/ICACCI.2017.8126106
Publication status	Published - 30-11-2017
Event	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 - Manipal, Mangalore, India Duration: 13-09-2017 → 16-09-2017

Conference

Conference	2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017
Country/Territory	India
City	Manipal, Mangalore
Period	13-09-17 → 16-09-17

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Computer Science Applications
Information Systems

UN SDGs

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

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10.1109/ICACCI.2017.8126106

Cite this

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title = "Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image",

abstract = "Cancer of blood-forming tissues is called Leukemia. This disease hinders the body's ability to fight infection. Leukemia can be categorized into many types. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the two main types. The blood cells' growth and bone marrow are affected by AML. Collection of myeloid blasts in the bone marrow is one of the main characteristics of AML. In this research, a novel method is analyzed to detect the presence of AML. The paper proposes a technique that automatically detects and segments the nucleus from white blood cells (WBCs) in the microscopic blood smear images. Segmentation and clustering is done using a K-Means algorithm, while classification is done using Support Vector Machine (SVM) with feature reduction.",

author = "Preetham Kumar and Udwadia, {Shazad Maneck}",

year = "2017",

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doi = "10.1109/ICACCI.2017.8126106",

language = "English",

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booktitle = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017",

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

address = "United States",

note = "2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017 ; Conference date: 13-09-2017 Through 16-09-2017",

}

Kumar, P & Udwadia, SM 2017, Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image. in 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers Inc., pp. 1803-1808, 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017, Manipal, Mangalore, India, 13-09-17. https://doi.org/10.1109/ICACCI.2017.8126106

Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image. / Kumar, Preetham; Udwadia, Shazad Maneck.
2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers Inc., 2017. p. 1803-1808.

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

TY - GEN

T1 - Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image

AU - Kumar, Preetham

AU - Udwadia, Shazad Maneck

PY - 2017/11/30

Y1 - 2017/11/30

N2 - Cancer of blood-forming tissues is called Leukemia. This disease hinders the body's ability to fight infection. Leukemia can be categorized into many types. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the two main types. The blood cells' growth and bone marrow are affected by AML. Collection of myeloid blasts in the bone marrow is one of the main characteristics of AML. In this research, a novel method is analyzed to detect the presence of AML. The paper proposes a technique that automatically detects and segments the nucleus from white blood cells (WBCs) in the microscopic blood smear images. Segmentation and clustering is done using a K-Means algorithm, while classification is done using Support Vector Machine (SVM) with feature reduction.

AB - Cancer of blood-forming tissues is called Leukemia. This disease hinders the body's ability to fight infection. Leukemia can be categorized into many types. Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) are the two main types. The blood cells' growth and bone marrow are affected by AML. Collection of myeloid blasts in the bone marrow is one of the main characteristics of AML. In this research, a novel method is analyzed to detect the presence of AML. The paper proposes a technique that automatically detects and segments the nucleus from white blood cells (WBCs) in the microscopic blood smear images. Segmentation and clustering is done using a K-Means algorithm, while classification is done using Support Vector Machine (SVM) with feature reduction.

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DO - 10.1109/ICACCI.2017.8126106

M3 - Conference contribution

AN - SCOPUS:85042802245

VL - 2017-January

SP - 1803

EP - 1808

BT - 2017 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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Y2 - 13 September 2017 through 16 September 2017

ER -

Automatic detection of Acute Myeloid Leukemia from microscopic blood smear image

Abstract

Conference

All Science Journal Classification (ASJC) codes

UN SDGs

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