Abstract
The objective of this paper is to introduce a rough neural intelligent approach for rule generation and image classification. Hybridization of intelligent computing techniques has been applied to see their ability and accuracy to classify breast cancer images into two outcomes: malignant cancer or benign cancer. Algorithms based on fuzzy image processing are first applied to enhance the contrast of the whole original image; to extract the region of interest and to enhance the edges surrounding that region. Then, we extract features characterizing the underlying texture of the regions of interest by using the gray-level co-occurrence matrix. Then, the rough set approach to attribute reduction and rule generation is presented. Finally, rough neural network is designed for discrimination of different regions of interest to test whether they represent malignant cancer or benign cancer. Rough neural network is built from rough neurons, each of which can be viewed as a pair of sub-neurons, corresponding to the lower and upper bounds.
To evaluate performance of the presented rough neural approach, we run tests over different mammogram images. The experimental results show that the overall classification accuracy offered by rough neural approach is high compared with other intelligent techniques.