An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks
Keywords:Extracted features, artificial neural networks, morphological operations, mammogram images
AbstractBreast 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.
Download data is not yet available.
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