Neural Systems Approach for Ammography Finding by Utilizing Wavelet Features

A. Mallareddy, A. Priyanka

Abstract: We present an application of artificial neural networks to mammographic images, aimed at improving early detection of sensitivity to breast cancer. The proposed application consists of two main steps: a pre-treatment step whose role is to extract the characteristics of the available mammographic images using the wavelet and co-occurrence (GLCM) matrices approach and a classification step based on an artificial neural network that uses these characteristics as input vectors for its training algorithm. The output of the training phase of this model is a categorization of the pre-treated images into three main groups: normal, benign and malignant. After the training phase, the network can be used in order to label new and unseen images as one of these three types.

Keywords: Breast Cancer, Neural Networks, Mammographic Images, Wavelet process, GLCM Classification

How to Cite?: A. Mallareddy, A. Priyanka, "Neural Systems Approach for Ammography Finding by Utilizing Wavelet Features", Volume 3 Issue 11, November 2014, International Journal of Science and Research (IJSR), Pages: 826-829, https://www.ijsr.net/getabstract.php?paperid=OCT14879, DOI: https://dx.doi.org/10.21275/OCT14879