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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Intelligent System for Detection of Micro-Calcification in Breast Cancer

Author 1: M. Abdul Rehman
Author 2: Jamil Ahmed
Author 3: Ahmed Waqas
Author 4: Ajmal Sawand

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2017.080751

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 8 Issue 7, 2017.

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Abstract: Recently; medical image mining has become one of the well-recognized research area(s) of machine learning and artificial intelligence techniques have been vastly used in various computer added diagnostic systems. Specifically; breast cancer classification problem is considered as one of the most significant problems. For instance, complex, diverse and heterogamous malignant features of micro-calcification in DICOM (Digital Communication in Medicine) images of mammography are very difficult to classify because the persistence of noise in mammogram images creates lots of confusions for doctors. In order to reduce the chances of misdiagnosis and to discernment the difference between malignant and benign lesions of micro-calcification this paper proposes a system so called “Intelligent System For Detection of Micro-Calcification in Breast Cancer” by considering all above stated problems. Overall our system comprises over three main stages. In first stage, adaptive threshold algorithm is used to reduce the noise, and canny edge detection algorithm is used to detect the edges of every macro or micro classification. In second stage, deginated as feature selection is done by using auto-crop algorithm, which crops all types of calcifications and lesions by proposed algorithm so called CFEDNN (Calcification Feature Extraction Deep Neural Networks) which is designed to avoid the manual ROIs (Region of Interest). Decision model is constructed by using DNN (Deep Neural Networks) and the best classification accuracy is measured as 95.6%.

Keywords: Medical image mining; machine learning; feature extraction; classification; Digital Communication in Medicine (DICOM)

M. Abdul Rehman, Jamil Ahmed, Ahmed Waqas and Ajmal Sawand, “Intelligent System for Detection of Micro-Calcification in Breast Cancer” International Journal of Advanced Computer Science and Applications(IJACSA), 8(7), 2017. http://dx.doi.org/10.14569/IJACSA.2017.080751

@article{Rehman2017,
title = {Intelligent System for Detection of Micro-Calcification in Breast Cancer},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2017.080751},
url = {http://dx.doi.org/10.14569/IJACSA.2017.080751},
year = {2017},
publisher = {The Science and Information Organization},
volume = {8},
number = {7},
author = {M. Abdul Rehman and Jamil Ahmed and Ahmed Waqas and Ajmal Sawand}
}


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