Medical Journal of Tabriz University of Medical Sciences and Health Services

Medical Journal of Tabriz University of Medical Sciences and Health Services. 2020;42(3): 281-286.
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Original Article

Mass smart detection for breast cancer diagnosis in mammographic images

Iman Abaspur Kazerouni ORCID logo, Hadi Mahdipour* ORCID logo, Fateme Hourali ORCID logo

1 Department of Electrical and Computer Engineering, Esfarayen University of Technology, Esfarayen, Iran
*Corresponding author; E-mail:


Background: Breast cancer is one of the most common types of cancer among women. Mammography is the most standard method for diagnosing breast cancer, which can be minimize the human error using computer systems of human error.

Methods: In this paper, using the image processing techniques, the mass was detected and identified in the photograph and then intelligent system outlined its margin After removing the image noise, using the fuzzy inference system, the fuzzy edge improvement has been applied and then using the coordinate logic filter, the mass areas have been detected and shown in the image.

Results: The proposed smart system have p <0.001 for the correct diagnosis compared to the human diagnostic methods.

Conclusion: The smart system results have been tested on 322 MIAS database images. In this database, 120 cases have benign and malignant tumors and 202 are healthy. The smart system was able to detect 115 cancer cases (true positive) and 190 healthy people (true negative) correctly. The number of false positive and false negative are 12 and 5, respectively. Therefore, the accuracy of the smart system for the database is 95%, and the sensitivity and specificity are 96% and 94%, respectively.

Keywords: Breast Cancer, Mass, Mammography, Smart System
How to cite this article: Abaspur Kazerouni I, Mahdipour Hosseinabad H, Hourali F. [Mass Smart Detection for Breast Cancer Diagnosis in Mammographic Images]. Med J Tabriz Uni Med Sciences Health Services. 2020 August-September; 42(3): 281-286. Persian.
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Submitted: 29 Aug 2018
ePublished: 18 Jul 2020
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