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

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Cite this article

@article{cai-110199,
  author  = {Heba Al-Hiary and Basim Alhadidi and Malik Braik},
  title   = {An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks},
  journal = {Computing and Informatics},
  volume  = {31},
  number  = {1},
  pages   = {225--244},
  year    = {2012},
  issn    = {1335-9150},
  url     = {https://cai.type.sk/content/2012/1/an-implemented-approach-for-potentially-breast-cancer-detection-using-extracted-features-and-artificial-neural-networks/}
}
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