Doctoral Dissertations

Keywords and Phrases

Cervical Cancer; Decision Support Systems; Deep Learning; Localized Features Fusion; Medical Imaging; Skin Cancer

Abstract

"Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible.

This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations.

For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method"--Abstract, page iv.

Advisor(s)

Stanley, R. Joe

Committee Member(s)

Moss, Randy Hays, 1953-
Stoecker, William V.
Wunsch, Donald C.
Shrestha, Bijaya
Samaranayake, V. A.

Department(s)

Electrical and Computer Engineering

Degree Name

Ph. D. in Computer Engineering

Sponsor(s)

U.S. National Institute of Health Intramural Research Program
National Library of Medicine (U.S. )
Lister Hill National Center for Biomedical Communications

Publisher

Missouri University of Science and Technology

Publication Date

Spring 2018

Journal article titles appearing in thesis/dissertation

  • Fuzzy color clustering for melanoma diagnosis in dermoscopy images
  • Algorithm enhancements for improvement of localized classification of uterine cervical cancer digital histology images
  • Classification of uterine cervical cancer digital histology images using a hybrid deep learning and handcrafted features approach

Pagination

xiii, 97 pages

Note about bibliography

Includes bibliographic references.

Rights

© 2018 Haidar Ali Almubarak, All rights reserved.

Document Type

Dissertation - Open Access

File Type

text

Language

English

Thesis Number

T 11261

Electronic OCLC #

1041857023

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