Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif

Breast cancer is an abnormal cells that forms in breast of human body. Breast cancer in Malaysia is a major cancer among women, followed by cervical cancer. Mammogram image is needed by radiologist for breast cancer diagnosis, mammogram is considered to be the most popular and accurate method of can...

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Main Author:	Ahmad Latif, Najihah
Format:	Thesis
Language:	English
Published:	2020
Subjects:	Instruments and machines Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/31558/1/31558.pdf
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id	my-uitm-ir.31558
record_format	uketd_dc
spelling	my-uitm-ir.315582020-06-26T04:16:23Z Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif 2020 Ahmad Latif, Najihah Instruments and machines Algorithms Computer applications to medicine. Medical informatics Breast cancer is an abnormal cells that forms in breast of human body. Breast cancer in Malaysia is a major cancer among women, followed by cervical cancer. Mammogram image is needed by radiologist for breast cancer diagnosis, mammogram is considered to be the most popular and accurate method of cancer prevention However, mammography images has a limitation such that it cannot detect any type of breast cancer, but because of its cheap and low complexity, it is still widely used in this world for breast cancer detection. In addition, the process of detecting tumour in dense breast tissue is not an easy process, as there is a weak contrast among their fatty tissue in mammograms. Several image processing techniques are currently being proposed to classify tumours in mammograms. Hence, this study purpose to implement image processing technique in classifying cancer in breast mammography image. This study used dataset which is composed of cancer and not cancer images are obtained from Mammographic Image Analysis Society (MIAS) dataset. For pre-processing, the image from the input is process using Image Enhancement, Image Thresholding and Image Segmentation technique Next, GLCM is used for the purpose of extracting the features from the mammography images and KNN classifier is used for the classification. Based on the testing that have been conducted on 113 images and the system achieved accuracy result of 57.52%. All in all, GLCM has been extract a total of 12 features from the image. About 65 total number of true result out of 113 mammography images have been test. This prototype met the objective to test the KNN classification technique accuracy. This KNN classifier and features in extraction process is not suitable for this project. For future enhancement, breast cancer classification can be test on other segmentation and machine learning technique in order to increase the accuracy. 2020 Thesis https://ir.uitm.edu.my/id/eprint/31558/ https://ir.uitm.edu.my/id/eprint/31558/1/31558.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	Instruments and machines Algorithms Instruments and machines
spellingShingle	Instruments and machines Algorithms Instruments and machines Ahmad Latif, Najihah Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif
description	Breast cancer is an abnormal cells that forms in breast of human body. Breast cancer in Malaysia is a major cancer among women, followed by cervical cancer. Mammogram image is needed by radiologist for breast cancer diagnosis, mammogram is considered to be the most popular and accurate method of cancer prevention However, mammography images has a limitation such that it cannot detect any type of breast cancer, but because of its cheap and low complexity, it is still widely used in this world for breast cancer detection. In addition, the process of detecting tumour in dense breast tissue is not an easy process, as there is a weak contrast among their fatty tissue in mammograms. Several image processing techniques are currently being proposed to classify tumours in mammograms. Hence, this study purpose to implement image processing technique in classifying cancer in breast mammography image. This study used dataset which is composed of cancer and not cancer images are obtained from Mammographic Image Analysis Society (MIAS) dataset. For pre-processing, the image from the input is process using Image Enhancement, Image Thresholding and Image Segmentation technique Next, GLCM is used for the purpose of extracting the features from the mammography images and KNN classifier is used for the classification. Based on the testing that have been conducted on 113 images and the system achieved accuracy result of 57.52%. All in all, GLCM has been extract a total of 12 features from the image. About 65 total number of true result out of 113 mammography images have been test. This prototype met the objective to test the KNN classification technique accuracy. This KNN classifier and features in extraction process is not suitable for this project. For future enhancement, breast cancer classification can be test on other segmentation and machine learning technique in order to increase the accuracy.
format	Thesis
qualification_level	Bachelor degree
author	Ahmad Latif, Najihah
author_facet	Ahmad Latif, Najihah
author_sort	Ahmad Latif, Najihah
title	Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif
title_short	Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif
title_full	Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif
title_fullStr	Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif
title_full_unstemmed	Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif
title_sort	breast cancer classification using mammography image and k-nearest neighbour / najihah ahmad latif
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/31558/1/31558.pdf
_version_	1783734128713138176

Breast cancer classification using mammography image and K-Nearest Neighbour / Najihah Ahmad Latif

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