A New Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks

Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segmentation and detection of B-cancer disease. Studies show that relying on pure nakedeye 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. As a testbed we use a set of mammogram images taken from the Medical Hussein City in Jordan. This study utilizes morphological operations as a segmentation approach with Artificial Neural Network (ANN) as a classifier tool. 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%; moreover, the overall accuracy is convinced and satisfied in all cases. 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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