Breast cancer identification based on artificial intelligent system

Sustainable Engineering and Innovation

View Publication Info
 
 
Field Value
 
Title Breast cancer identification based on artificial intelligent system
 
Creator Silman, Hassan Khalil
Ali, Akbas Ezaldeen
 
Description Worldwide, breast cancer causes a high mortality rate. Early diagnosis is important for treatment, but high-density breast tissues are difficult to analyze. Computer-assisted identification systems were introduced to classify by fine needle aspirates FNA with features that better represent the images to be classified as a major challenge. This work is fully automated, and it does not require any manual intervention from user. In this analysis, various texture definitions for the portrayal of breast tissue density on mammograms are examined within addition to contrasting them with other techniques. We have created an algorithm that can be divided into three classes: fatty, fatty-glandular and dense-glandular. The suggested system works in a spatial-related domain and it results with extreme immunity to noise and background area, with a high rate of precision.
 
Publisher Research and Development Academy
 
Date 2020-07-14
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier https://sei.ardascience.com/index.php/journal/article/view/108
10.37868/sei.v2i2.108
 
Source Sustainable Engineering and Innovation; Vol. 2 No. 2 (2020); 109-118
2712-0562
10.37868/sei.v2i2
 
Language eng
 
Relation https://sei.ardascience.com/index.php/journal/article/view/108/75
 
Rights Copyright (c) 2020 Hassan Khalil Silman, Akbas Ezaldeen Ali
https://creativecommons.org/licenses/by/4.0
 

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