Abstract
Breast cancer today is the leading cause of death amongst cancer patients inflicting women around the world. Breast cancer is the most common cancer in women worldwide. It is also the principle cause of death from cancer among women globally. Early detection of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Classification of cancer data helps widely in detection of the disease and it can be achieved using many techniques such as Perceptron which is an Artificial Neural Network (ANN) classification technique. In this paper, we proposed a new hybrid algorithm by combining the perceptron algorithm and the feature extraction algorithm after applying the Scale Invariant Feature Transform (SIFT) algorithm in order to classify magnetic resonance imaging (MRI) breast cancer images. The proposed algorithm is called breast MRI cancer classifier (BMRICC) and it has been tested tested on 281 MRI breast images (138 abnormal and 143 normal). The numerical results of the general performance of the BMRICC algorithm and the comparasion results between it and other 5 benchmark classifiers show that, the BMRICC algorithm is a promising algorithm and its performance is better than the other algorithms.
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ElNawasany, A.M., Ali, A.F., Waheed, M.E. (2014). A Novel Hybrid Perceptron Neural Network Algorithm for Classifying Breast MRI Tumors. In: Hassanien, A.E., Tolba, M.F., Taher Azar, A. (eds) Advanced Machine Learning Technologies and Applications. AMLTA 2014. Communications in Computer and Information Science, vol 488. Springer, Cham. https://doi.org/10.1007/978-3-319-13461-1_34
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DOI: https://doi.org/10.1007/978-3-319-13461-1_34
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