Breast cancer detection system based oncomprehensive wavelet features of mammogram images and neural network

Abstract EN

The diagnosis system of mammogram images of breast cancer mainly consists of image preprocessing which includes image enhancement and image deionizing feature extraction, and classification.

The feature extraction plays a very important role in breast cancer classification system.

This paper is presented the comprehensive statistical texture feature extraction method uses Haar DWT.

The proposed algorithm calculates max and min medians as well as the standard deviation and average of detail images obtained from wavelet filters, then comes by feature vectors and attempts to classify the given mammogram using a probabilistic neural network with a single hidden layer.

In this study along with the proposal of using median of optimum points as the basic feature and its comparison with the rest of the statistical features in the wavelet field, the relational advantages of Haar wavelet is investigated.

This method has been experimented on a number of malignant and benign mammogram.

The experimentation shows that proposed method improves the performance.

Amongst the other advantages, high speed and low calculating load are prominent because it does not involveimage segmentation, hence reduce computational complexity.