Document Type

Thesis - Open Access

Award Date

2017

Degree Name

Master of Science (MS)

Department

Electrical Engineering and Computer Science

First Advisor

Sung Y. Shin

Keywords

Artificial Neural Network, Computer Aided Diagnosis System, Machine Learning, Medical Image Processing, Multilayer Perceptron

Abstract

Most recently developed Computer Aided Diagnosis (CAD) systems and their related research is based on medical images that are usually obtained through conventional imaging techniques such as Magnetic Resonance Imaging (MRI), x-ray mammography, and ultrasound. With the development of a new imaging technology called Microwave Tomography Imaging (MTI), it has become inevitable to develop a CAD system that can show promising performance using new format of data. The platform can have a flexibility on its input by adopting Artificial Neural Network (ANN) as a classifier. Among the various phases of CAD system, we have focused on optimizing the classification phase that directly affects its performance. In this paper, we present the optimized Multilayer Perceptron (MLP) binary classifier, which can be plugged into the CAD system, that uses Dynamic Learning Rate (DLR) for alleviating local minima problem. The proposed classifier has an optimized size of neural network so that it will not fall into indeterminate equation problem by having reasonable amount of weights between each perceptron. Also, the proposed model will dynamically assign a learning rate onto each training points in the way that model earmarks a higher learning rate onto each training points belonging into minority class in order to escape from local minima which is a typical jeopardy of MLP. In experiment, we evaluate performance of our model with following measures; precision, recall, specificity, accuracy, and Matthews Correlation Coefficient (MCC) and compare them to that of work by Samaneh et al. The results show that our model outperforms existing model not only for the performance such as recall, specificity, accuracy, and precision, but also for the quality, and thus it empowers physicians to make better decision on breast cancer screening in early stage, as it also alleviates the cost burden from the patients.

Library of Congress Subject Headings

Perceptrons.
Breast -- Imaging.
Breast -- Tomography.
Breast -- Cancer -- Diagnosis.
Medical screening.

Description

Includes bibliographical references (pages 23-25).

Format

application/pdf

Number of Pages

35

Publisher

South Dakota State University

Rights

In Copyright - Educational Use Permitted
http://rightsstatements.org/vocab/InC-EDU/1.0/

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