Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation
Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extract...
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doaj-3f2667cf803e493c837d119327d50b3f2021-06-01T07:00:37ZengUniveristy of Kurdistan HewlerUKH Journal of Science and Engineering2520-77922020-12-014717818710.25079/ukhjse.v4n2y2020.pp178-187Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet TransformationBestan Maaroof Bahaalddin0https://orcid.org/0000-0002-6953-8269Hawkar Omar Ahmed1https://orcid.org/0000-0002-2945-479XDepartment of Information Technology, College of Commerce, University of Sulaimani, Sulaimani City, Kurdistan Region- F. R. IraqDepartment of Information Technology, College of Commerce, University of Sulaimani, Sulaimani City, Kurdistan Region- F. R. Iraq.Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extraction, and finally classification and performance analysis. The case sample mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. In the preprocessing phase the ROI is cropped and resized by 128 x 128. at the very beginning of the feature extraction process, we have applied Haar Wavelet Transform (HWT) for five levels and, in each level, Discrete Cosine Transform applied with various selection of coefficients. After that, different types of features are fed into the feature similarity measure City Block for the diagnosis of breast cancer. The images are of two classes benign and malignant classes. Finally, K-Nearest Number is employed here as a classifier. In our proposed system, we found competitive results.https://journals.ukh.edu.krd/index.php/ukhjse/article/view/248discrete cosine transformbreast masshaar wavelet transformfeature extractionmammogram |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Bestan Maaroof Bahaalddin Hawkar Omar Ahmed |
spellingShingle |
Bestan Maaroof Bahaalddin Hawkar Omar Ahmed Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation UKH Journal of Science and Engineering discrete cosine transform breast mass haar wavelet transform feature extraction mammogram |
author_facet |
Bestan Maaroof Bahaalddin Hawkar Omar Ahmed |
author_sort |
Bestan Maaroof Bahaalddin |
title |
Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation |
title_short |
Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation |
title_full |
Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation |
title_fullStr |
Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation |
title_full_unstemmed |
Breast Mass Classification Based on Hybrid Discrete Cosine Transformation–Haar Wavelet Transformation |
title_sort |
breast mass classification based on hybrid discrete cosine transformation–haar wavelet transformation |
publisher |
Univeristy of Kurdistan Hewler |
series |
UKH Journal of Science and Engineering |
issn |
2520-7792 |
publishDate |
2020-12-01 |
description |
Mammography is the most effective procedure for the early detection of breast cancer. In this paper an efficient a Computer Aided Diagnosis (CADx) system is proposed to discriminate between benign and malignant. The system comprises mainly of three steps: preprocessing of the images, feature extraction, and finally classification and performance analysis. The case sample mammographic images, originating from the mini MIAS (Mammographic Image Analysis Society) database. In the preprocessing phase the ROI is cropped and resized by 128 x 128. at the very beginning of the feature extraction process, we have applied Haar Wavelet Transform (HWT) for five levels and, in each level, Discrete Cosine Transform applied with various selection of coefficients. After that, different types of features are fed into the feature similarity measure City Block for the diagnosis of breast cancer. The images are of two classes benign and malignant classes. Finally, K-Nearest Number is employed here as a classifier. In our proposed system, we found competitive results. |
topic |
discrete cosine transform breast mass haar wavelet transform feature extraction mammogram |
url |
https://journals.ukh.edu.krd/index.php/ukhjse/article/view/248 |
work_keys_str_mv |
AT bestanmaaroofbahaalddin breastmassclassificationbasedonhybriddiscretecosinetransformationhaarwavelettransformation AT hawkaromarahmed breastmassclassificationbasedonhybriddiscretecosinetransformationhaarwavelettransformation |
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1721411013790662656 |