Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin

Breast cancer is the one of the most cancer that frequents suffered by women nowadays, throughout the world. This diseases can be distinguish by do persistent clinical breast test and breast screening. Mammography images is the most effective and widely used method for detecting and screening the ab...

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Main Author:	Sahrudin, Nur Syafiqah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Neural networks (Computer science) Data processing
Online Access:	https://ir.uitm.edu.my/id/eprint/31592/1/31592.pdf
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id	my-uitm-ir.31592
record_format	uketd_dc
spelling	my-uitm-ir.315922020-06-26T03:35:37Z Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin 2020 Sahrudin, Nur Syafiqah Neural networks (Computer science). Data processing Computer applications to medicine. Medical informatics Radiography. Mammography Breast cancer is the one of the most cancer that frequents suffered by women nowadays, throughout the world. This diseases can be distinguish by do persistent clinical breast test and breast screening. Mammography images is the most effective and widely used method for detecting and screening the abnormalities in breast. However, the low quality of mammography images leads to the tedious and challenging task in diagnosis process. In addition, the mammographic images are too complex to interpret it. Thus, the implementation of image processing in medical images can help the medical practitioners in diagnosis process. Hence this study propose a breast cancer classification using mammogram images. These prototype systems are using enhancement, segmentation, and feature extraction and classification method. This enhancement is using median filtering method to noise removal. The segmentation of mammogram images has been playing important part to improve the detection of breast cancer. The segmentation method used is Region props, this process need to segment the tumour part. The extraction features are extracted from the segmented area of breast by using GLCM method. The last step is classifying the cancerous or non-cancerous by using Support Vector Machine (SVM) classifier. The developed prototype technique is tested using 112 mammography images which are obtained from MIAS online database. This implementation of GLCM for feature extraction and SVM classifier has yield 85% in accuracy percentage. It show that, SVM classifier is potential to classify breast cancer. 2020 Thesis https://ir.uitm.edu.my/id/eprint/31592/ https://ir.uitm.edu.my/id/eprint/31592/1/31592.pdf text en public degree Universiti Teknologi MARA, Cawangan Melaka Faculty of Computer and Mathematical Sciences Abu Mangshor, Nur Nabilah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Abu Mangshor, Nur Nabilah
topic	Neural networks (Computer science) Data processing Neural networks (Computer science) Data processing Neural networks (Computer science) Data processing
spellingShingle	Neural networks (Computer science) Data processing Neural networks (Computer science) Data processing Neural networks (Computer science) Data processing Sahrudin, Nur Syafiqah Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin
description	Breast cancer is the one of the most cancer that frequents suffered by women nowadays, throughout the world. This diseases can be distinguish by do persistent clinical breast test and breast screening. Mammography images is the most effective and widely used method for detecting and screening the abnormalities in breast. However, the low quality of mammography images leads to the tedious and challenging task in diagnosis process. In addition, the mammographic images are too complex to interpret it. Thus, the implementation of image processing in medical images can help the medical practitioners in diagnosis process. Hence this study propose a breast cancer classification using mammogram images. These prototype systems are using enhancement, segmentation, and feature extraction and classification method. This enhancement is using median filtering method to noise removal. The segmentation of mammogram images has been playing important part to improve the detection of breast cancer. The segmentation method used is Region props, this process need to segment the tumour part. The extraction features are extracted from the segmented area of breast by using GLCM method. The last step is classifying the cancerous or non-cancerous by using Support Vector Machine (SVM) classifier. The developed prototype technique is tested using 112 mammography images which are obtained from MIAS online database. This implementation of GLCM for feature extraction and SVM classifier has yield 85% in accuracy percentage. It show that, SVM classifier is potential to classify breast cancer.
format	Thesis
qualification_level	Bachelor degree
author	Sahrudin, Nur Syafiqah
author_facet	Sahrudin, Nur Syafiqah
author_sort	Sahrudin, Nur Syafiqah
title	Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin
title_short	Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin
title_full	Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin
title_fullStr	Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin
title_full_unstemmed	Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin
title_sort	mammogram breast cancer classification using support vector machines (svm) / nur syafiqah sahrudin
granting_institution	Universiti Teknologi MARA, Cawangan Melaka
granting_department	Faculty of Computer and Mathematical Sciences
publishDate	2020
url	https://ir.uitm.edu.my/id/eprint/31592/1/31592.pdf
_version_	1783734133105623040

Mammogram breast cancer classification using Support Vector Machines (SVM) / Nur Syafiqah Sahrudin

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