ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters

Shahriar Kabir; Mohammad Ahad

doi:10.1109/BIBE50027.2020.00151

ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters

Shahriar Kabir
, Mohammad Ahad

Electrical & Computer Engineering

Georgia Southern University

Research output: Contribution to book or proceeding › Conference article › peer-review

4 Scopus citations

Abstract

Electrical Impedance Myography (EIM) is a painless, non-invasive electrical bio-impedance measurement technique for assessing neurological disease states. In this electro-physiological technique, the EIM parameters, namely resistance, reactance, and phase magnitude, depend on several anatomic factors such as muscle girth, skin thickness, fat thickness. EIM may also be affected by several non-anatomic factors like frequency, electrode size, and inter-electrode distance. This paper explores the female breast tumor type classification by extracting EIM parameters from a 3D model of the female breast. The extracted EIM parameters from the simulation employ an artificial neural network (ANN) to identify benign and malignant tumor types. A 3D finite element (FEM) model of a female breast with a rectangular shape of electrodes are developed with a base shape of an 80 mm outer radius. The subsequent shapes are designed as -20% and +20% of the base shape, as mentioned above. This paper presents the EIM parameters that can classify female breast tumor types with an accuracy of 96.2% using an ANN.

Original language	English
Title of host publication	Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	890-893
Number of pages	4
ISBN (Electronic)	9781728195742
DOIs	https://doi.org/10.1109/BIBE50027.2020.00151
State	Published - Oct 2020
Event	20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 - Virtual, Cincinnati, United States Duration: Oct 26 2020 → Oct 28 2020

Publication series

Name	Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

Conference

Conference	20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020
Country/Territory	United States
City	Virtual, Cincinnati
Period	10/26/20 → 10/28/20

UN SDGs

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

SDG 3 Good Health and Well-being

Scopus Subject Areas

Biotechnology
Genetics
Molecular Biology
Artificial Intelligence
Computer Science Applications
Biomedical Engineering
Modeling and Simulation
Health Informatics

Keywords

ANN
Artificial neural network
Benign tumor
Breast tumor
EIM
Electrical impedance myography
FEM
Female breast cancer
Finite element method
Machine learning
Malignant tumor
ML
Tumor location prediction.
Tumor type classification

Access to Document

10.1109/BIBE50027.2020.00151

Cite this

Kabir, S., & Ahad, M. (2020). ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020 (pp. 890-893). Article 9288067 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBE50027.2020.00151

Kabir, Shahriar ; Ahad, Mohammad. / ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters. Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. pp. 890-893 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020).

@inproceedings{25498950c93a4848b12805ada4b9b897,

title = "ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters",

abstract = "Electrical Impedance Myography (EIM) is a painless, non-invasive electrical bio-impedance measurement technique for assessing neurological disease states. In this electro-physiological technique, the EIM parameters, namely resistance, reactance, and phase magnitude, depend on several anatomic factors such as muscle girth, skin thickness, fat thickness. EIM may also be affected by several non-anatomic factors like frequency, electrode size, and inter-electrode distance. This paper explores the female breast tumor type classification by extracting EIM parameters from a 3D model of the female breast. The extracted EIM parameters from the simulation employ an artificial neural network (ANN) to identify benign and malignant tumor types. A 3D finite element (FEM) model of a female breast with a rectangular shape of electrodes are developed with a base shape of an 80 mm outer radius. The subsequent shapes are designed as -20\% and +20\% of the base shape, as mentioned above. This paper presents the EIM parameters that can classify female breast tumor types with an accuracy of 96.2\% using an ANN.",

keywords = "ANN, Artificial neural network, Benign tumor, Breast tumor, EIM, Electrical impedance myography, FEM, Female breast cancer, Finite element method, Machine learning, Malignant tumor, ML, Tumor location prediction., Tumor type classification",

author = "Shahriar Kabir and Mohammad Ahad",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020 ; Conference date: 26-10-2020 Through 28-10-2020",

year = "2020",

month = oct,

doi = "10.1109/BIBE50027.2020.00151",

language = "English",

series = "Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020",

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

pages = "890--893",

booktitle = "Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020",

address = "United States",

}

Kabir, S & Ahad, M 2020, ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters. in Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020., 9288067, Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020, Institute of Electrical and Electronics Engineers Inc., pp. 890-893, 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020, Virtual, Cincinnati, United States, 10/26/20. https://doi.org/10.1109/BIBE50027.2020.00151

ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters. / Kabir, Shahriar; Ahad, Mohammad.
Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. p. 890-893 9288067 (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020).

Research output: Contribution to book or proceeding › Conference article › peer-review

TY - GEN

T1 - ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters

AU - Kabir, Shahriar

AU - Ahad, Mohammad

PY - 2020/10

Y1 - 2020/10

N2 - Electrical Impedance Myography (EIM) is a painless, non-invasive electrical bio-impedance measurement technique for assessing neurological disease states. In this electro-physiological technique, the EIM parameters, namely resistance, reactance, and phase magnitude, depend on several anatomic factors such as muscle girth, skin thickness, fat thickness. EIM may also be affected by several non-anatomic factors like frequency, electrode size, and inter-electrode distance. This paper explores the female breast tumor type classification by extracting EIM parameters from a 3D model of the female breast. The extracted EIM parameters from the simulation employ an artificial neural network (ANN) to identify benign and malignant tumor types. A 3D finite element (FEM) model of a female breast with a rectangular shape of electrodes are developed with a base shape of an 80 mm outer radius. The subsequent shapes are designed as -20% and +20% of the base shape, as mentioned above. This paper presents the EIM parameters that can classify female breast tumor types with an accuracy of 96.2% using an ANN.

AB - Electrical Impedance Myography (EIM) is a painless, non-invasive electrical bio-impedance measurement technique for assessing neurological disease states. In this electro-physiological technique, the EIM parameters, namely resistance, reactance, and phase magnitude, depend on several anatomic factors such as muscle girth, skin thickness, fat thickness. EIM may also be affected by several non-anatomic factors like frequency, electrode size, and inter-electrode distance. This paper explores the female breast tumor type classification by extracting EIM parameters from a 3D model of the female breast. The extracted EIM parameters from the simulation employ an artificial neural network (ANN) to identify benign and malignant tumor types. A 3D finite element (FEM) model of a female breast with a rectangular shape of electrodes are developed with a base shape of an 80 mm outer radius. The subsequent shapes are designed as -20% and +20% of the base shape, as mentioned above. This paper presents the EIM parameters that can classify female breast tumor types with an accuracy of 96.2% using an ANN.

KW - ANN

KW - Artificial neural network

KW - Benign tumor

KW - Breast tumor

KW - EIM

KW - Electrical impedance myography

KW - FEM

KW - Female breast cancer

KW - Finite element method

KW - Machine learning

KW - Malignant tumor

KW - ML

KW - Tumor location prediction.

KW - Tumor type classification

UR - https://www.scopus.com/pages/publications/85099544724

U2 - 10.1109/BIBE50027.2020.00151

DO - 10.1109/BIBE50027.2020.00151

M3 - Conference article

AN - SCOPUS:85099544724

T3 - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

SP - 890

EP - 893

BT - Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 20th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2020

Y2 - 26 October 2020 through 28 October 2020

ER -

Kabir S, Ahad M. ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters. In Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. p. 890-893. 9288067. (Proceedings - IEEE 20th International Conference on Bioinformatics and Bioengineering, BIBE 2020). doi: 10.1109/BIBE50027.2020.00151

ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters

Abstract

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UN SDGs

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Keywords

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