TY - GEN
T1 - ANN Classification of Female Breast Tumor Type Prediction Using EIM Parameters
AU - Kabir, Shahriar
AU - Ahad, Mohammad
N1 - Publisher Copyright:
© 2020 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85099544724&partnerID=8YFLogxK
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 -
