Novel feature set for automatic assessment and classification of breast tumor through back propagation artificial neural network

Abstract

Breast cancer is a deadly disease having high mortality rate from several years. It is second and fourth leading disease in the world and India respectively as per the WHO. The conventional techniques are unsupervised to classify breast cancer that involves erroneous, laborious and demanding inevitable presence of clinician. It is also experimented on small dataset and the accuracy of the previous classifier methods was unsatisfactory. To overcome these problems, we have experimented on large dataset and extracted several features such as area, convex area, bounding box, eccentricity, orientation, solidity, and perimeter, contour based fractal dimension etc. These feature set describes the size and geometrical shape of the tumor. The increase in feature set leads to increase in the accuracy of the classification. The automatic classification is based on multilayer back propagation artificial neural networks (ANN) algorithm. The breast cancer tumors have an important clue in its boundary, hence analysis of that plays a vital role for better identification of disease. The dataset is split into training and testing data on an around 1700 samples using 80-20 rule with different neural network architectures. Hence the accuracy of 98.11% has been achieved in the classification rate. The successful classification depends on the quality of the enhanced mammograms, localization of tumor and accurate segmentation. The image samples from MIAS, DDSM and local hospitals had been involved in the experiment.