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Application of Data Mining Algorithms for Mammogram Classification

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Authors
Radović, Miloš
Đoković, Marina
Peulić, Aleksandar
Filipović, Nenad
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Abstract
One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algori...thms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.

Source:
2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE), 2013
Publisher:
  • IEEE, New York
Funding / projects:
  • Semantic Infostructure interlinking an open source Finite Element tool and libraries with a model repository for the multi-scale Modelling and 3d visualization of the inner-ear (EU-600933)
  • Multiscale Methods and Their Applicatios in Nanomedicine (RS-174028)
  • Application of biomedical engineering for preclinical and clinical practice (RS-41007)

ISSN: 2471-7819

WoS: 000335217700025

Scopus: 2-s2.0-84894158902
[ Google Scholar ]
19
1
URI
https://gery.gef.bg.ac.rs/handle/123456789/603
Collections
  • Radovi istraživača
Institution/Community
Geografski fakultet
TY  - CONF
AU  - Radović, Miloš
AU  - Đoković, Marina
AU  - Peulić, Aleksandar
AU  - Filipović, Nenad
PY  - 2013
UR  - https://gery.gef.bg.ac.rs/handle/123456789/603
AB  - One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.
PB  - IEEE, New York
C3  - 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
T1  - Application of Data Mining Algorithms for Mammogram Classification
UR  - conv_1510
ER  - 
@conference{
author = "Radović, Miloš and Đoković, Marina and Peulić, Aleksandar and Filipović, Nenad",
year = "2013",
abstract = "One of the leading causes of cancer death among women is breast cancer. In our work we aim at proposing a prototype of a medical expert system (based on data mining techniques) that could significantly aid medical experts to detect breast cancer. This paper presents the CAD (computer aided diagnosis) system for the detection of normal and abnormal pattern in the breast. The proposed system consists of four major steps: the image preprocessing, the feature extraction, the feature selection and the classification process that classifies mammogram into normal (without tumor) and abnormal (with tumor) pattern. After removing noise from mammogram using the Discrete Wavelet Transformation (DWT), first is selected the region of interest (ROI). By identifying the boundary of the breast, it is possible to remove any artifact present outside the breast area, such as patient markings. Then, a total of 20 GLCM features are extracted from the ROI, which were used as inputs for classification algorithms. In order to compare the classification results, we used seven different classifiers. Normal breast images and breast image with masses (total 322 images) used as input images in this study are taken from the mini-MIAS database.",
publisher = "IEEE, New York",
journal = "2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)",
title = "Application of Data Mining Algorithms for Mammogram Classification",
url = "conv_1510"
}
Radović, M., Đoković, M., Peulić, A.,& Filipović, N.. (2013). Application of Data Mining Algorithms for Mammogram Classification. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE)
IEEE, New York..
conv_1510
Radović M, Đoković M, Peulić A, Filipović N. Application of Data Mining Algorithms for Mammogram Classification. in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE). 2013;.
conv_1510 .
Radović, Miloš, Đoković, Marina, Peulić, Aleksandar, Filipović, Nenad, "Application of Data Mining Algorithms for Mammogram Classification" in 2013 IEEE 13th International Conference on Bioinformatics and Bioengineering (BIBE) (2013),
conv_1510 .

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