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Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network

Mammograms can be used to check for breast cancer in women. In this paper, we have proposed breast cancer detection into two stages. In the first stage, mammograms have to classify into malignant and benign. While in second stage, the type of abnormality is detected. Features have been extracted usi...

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Main Authors: Talha, Muhammad, Sulong, Ghazali, Naveed, Nawazish, Jaffar, Arfan
Format: Article
Published: 2012
Subjects:
ZA Information resources
Online Access:http://eprints.utm.my/id/eprint/47169/
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spelling my.utm.471692019-03-31T08:34:33Z http://eprints.utm.my/id/eprint/47169/ Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network Talha, Muhammad Sulong, Ghazali Naveed, Nawazish Jaffar, Arfan ZA Information resources Mammograms can be used to check for breast cancer in women. In this paper, we have proposed breast cancer detection into two stages. In the first stage, mammograms have to classify into malignant and benign. While in second stage, the type of abnormality is detected. Features have been extracted using Discrete Wavelet Transform. These wavelet based features has been reduced using Principle Component Analysis. Those images which have been classified as malignant in the first stage are further classified into six classes to check its abnormality. It has been observed that the accuracy of classification of abnormalities is more than 90%. Mammographic Institute Society Analysis dataset is used for experimentation. 2012 Article PeerReviewed Talha, Muhammad and Sulong, Ghazali and Naveed, Nawazish and Jaffar, Arfan (2012) Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network. Information, 15 (2). pp. 707-719. ISSN 1343-4500
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
topic ZA Information resources
spellingShingle ZA Information resources
Talha, Muhammad
Sulong, Ghazali
Naveed, Nawazish
Jaffar, Arfan
Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
description Mammograms can be used to check for breast cancer in women. In this paper, we have proposed breast cancer detection into two stages. In the first stage, mammograms have to classify into malignant and benign. While in second stage, the type of abnormality is detected. Features have been extracted using Discrete Wavelet Transform. These wavelet based features has been reduced using Principle Component Analysis. Those images which have been classified as malignant in the first stage are further classified into six classes to check its abnormality. It has been observed that the accuracy of classification of abnormalities is more than 90%. Mammographic Institute Society Analysis dataset is used for experimentation.
format Article
author Talha, Muhammad
Sulong, Ghazali
Naveed, Nawazish
Jaffar, Arfan
author_facet Talha, Muhammad
Sulong, Ghazali
Naveed, Nawazish
Jaffar, Arfan
author_sort Talha, Muhammad
title Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
title_short Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
title_full Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
title_fullStr Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
title_full_unstemmed Malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
title_sort malignancy and abnormality detection of mammograms using discrete wavelet transformed features and neural network
publishDate 2012
url http://eprints.utm.my/id/eprint/47169/
_version_ 1643652249271926784
score 13.211869

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