Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network

Malaysian woman has one of every 19 opportunities to be this dreaded disease amid her lifetime. Breast cancer remains as one of the most crucial causes of morbidity and mortality around the world. Mammography is currently the standard breast cancer medical screening option, however it is not that ef...

Full description

Saved in:

Bibliographic Details
Main Author:	Ting, Fung Fung
Format:	Thesis
Published:	2019
Subjects:	QA75.5-76.95 Electronic computers Computer science
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-mmu-ep.7761
record_format	uketd_dc
spelling	my-mmu-ep.77612020-09-22T17:51:37Z Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network 2019-07 Ting, Fung Fung QA75.5-76.95 Electronic computers. Computer science Malaysian woman has one of every 19 opportunities to be this dreaded disease amid her lifetime. Breast cancer remains as one of the most crucial causes of morbidity and mortality around the world. Mammography is currently the standard breast cancer medical screening option, however it is not that effective for patient under 40 years old and dense breasts, less susceptible to small tumours (less than 1 mm, approximately 100,000 cells), and gives no indication of breast cancer. Manual cancer mass delineation through medical doctors is currently referred as the standard approach, but it is suggested to be time-consuming and operator dependent. In order to counter these issues, a deep learning module had been designed to assist doctors in aspects of breast cancer detection and 3D model view of real patient MRI images. Breast Cancer Classification and Visualisation through Transposed Deep Neural Network (BCCV-TDNN) system is divided into three main sections, namely, Feature Wise Spatial Pre-processing (FWSP), Feature Wise Transposed Deep Neural Networks (FWTDNN), and Implicit Volume Ray Cast Mesh Renderer (IVRCMR). The designed system is able to detect and classify the breast cancer masses and visualize the patient breast cancer atlas overlay with the detected tumour. BCC-TDNN is designed to detect and classify the suspicious lesion from mammography to assist medical experts. The application of Feature Wise Spatial Pre-processing (FWSP) is to denoise and enhances the input medical images. The processed input medical images are separated into training set, validation set, and testing dataset. The datasets distribution is applied to minimise the chances of overfitting for the deep neural network rate and regularization parameter by using 20% of the training set for validation and evaluate them in the testing set. 2019-07 Thesis http://shdl.mmu.edu.my/7761/ http://library.mmu.edu.my/library2/diglib/mmuetd/ phd doctoral Multimedia University Faculty of Engineering & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	QA75.5-76.95 Electronic computers Computer science
spellingShingle	QA75.5-76.95 Electronic computers Computer science Ting, Fung Fung Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
description	Malaysian woman has one of every 19 opportunities to be this dreaded disease amid her lifetime. Breast cancer remains as one of the most crucial causes of morbidity and mortality around the world. Mammography is currently the standard breast cancer medical screening option, however it is not that effective for patient under 40 years old and dense breasts, less susceptible to small tumours (less than 1 mm, approximately 100,000 cells), and gives no indication of breast cancer. Manual cancer mass delineation through medical doctors is currently referred as the standard approach, but it is suggested to be time-consuming and operator dependent. In order to counter these issues, a deep learning module had been designed to assist doctors in aspects of breast cancer detection and 3D model view of real patient MRI images. Breast Cancer Classification and Visualisation through Transposed Deep Neural Network (BCCV-TDNN) system is divided into three main sections, namely, Feature Wise Spatial Pre-processing (FWSP), Feature Wise Transposed Deep Neural Networks (FWTDNN), and Implicit Volume Ray Cast Mesh Renderer (IVRCMR). The designed system is able to detect and classify the breast cancer masses and visualize the patient breast cancer atlas overlay with the detected tumour. BCC-TDNN is designed to detect and classify the suspicious lesion from mammography to assist medical experts. The application of Feature Wise Spatial Pre-processing (FWSP) is to denoise and enhances the input medical images. The processed input medical images are separated into training set, validation set, and testing dataset. The datasets distribution is applied to minimise the chances of overfitting for the deep neural network rate and regularization parameter by using 20% of the training set for validation and evaluate them in the testing set.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ting, Fung Fung
author_facet	Ting, Fung Fung
author_sort	Ting, Fung Fung
title	Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
title_short	Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
title_full	Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
title_fullStr	Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
title_full_unstemmed	Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network
title_sort	breast cancer classification and visualisation using transposed deep neural network
granting_institution	Multimedia University
granting_department	Faculty of Engineering & Technology
publishDate	2019
_version_	1747829676254953472

Breast Cancer Classification And Visualisation Using Transposed Deep Neural Network

Similar Items