An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks

Authors

  • Heba Al-Hiary
  • Basim Alhadidi
  • Malik Braik

Keywords:

Extracted features, artificial neural networks, morphological operations, mammogram images

Abstract

Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segment and detect B-cancer disease. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image-processing-and artificial intelligence-based solutions for that task can be of great realistic significance. The presented approach itself scans the whole mammogram and performs filtering, segmentation, features extraction, and detection in a succession mode. The feasibility of the proposed approach was explored on 32 commonly virulent images, and the recognition rate achieved in the detection step is 100 %; further, the approach is able to give reliable results on distorted medical images, since the approach is subjected to a rectification step. Finally, this study is very effectual in decreasing mortality and increasing the quality of treatment of early onset of B-cancer.

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Author Biographies

Heba Al-Hiary

Al-Balqa Applied University
Al-Salt, Jordan

Basim Alhadidi

Al-Balqa Applied University
Al-Salt, Jordan

Malik Braik

University of Birmingham
United  Kingdom

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Published

2012-06-20

How to Cite

Al-Hiary, H., Alhadidi, B., & Braik, M. (2012). An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks. COMPUTING AND INFORMATICS, 31(1), 225–244. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/897