Term of Award

Spring 2020

Degree Name

Master of Science, Electrical Engineering

Document Type and Release Option

Thesis (restricted to Georgia Southern)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Electrical and Computer Engineering

Committee Chair

Mohammad Ahad

Committee Member 1

Sungkyun Lim

Committee Member 2

Adel El Shahat

Abstract

Electrical Impedance Myography (EIM) is a painless, non-invasive electrical bio-impedance measurement technique for assessing neurological disease states. In this electrophysiological 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 thesis paper presents two separate studies. The first study 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. The second study presents the female breast tumor location classification by extracting EIM parameters from a female breast 3D model and then use EIM parameters in an artificial neural network (ANN) to predict the locations of the female breast tumors. 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 thesis paper presents that the EIM parameters can classify female breast tumor types and predict locations of tumors using an ANN.

Research Data and Supplementary Material

No

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