Classification Using Two Layer Neural Network Back Propagation Algorithm

Abstract

Worldwide breast cancer is the most common form of cancer death occurring in 12.6% of women. This paper presents a cost effective approach to classify the normal, malignant and benign tumor using two layer neural network back propagation algorithm. Back propagation algorithm is used to train the neural network. Parallelization techniques speed up the computation process and as a result two layer neural networks outperform the previous work in terms of accuracy. Breast cancer tumor database used for the testing purpose is from the CIA machine learning repository. The highest accuracy of 97.12% is achieved using the two layer neural network back propagation algorithm.

A Research on Mammograms Classification using Wavelets

“…This helps the specialist to know about the typical behavior of disease [4]. It decreases the risk of mistakes that are made by human while reading the mammograms and also gives good results in characterization into benevolent and harmful anomaly.…”

“…These texture based elements are extracted using gray level cooccurance matrix (GLCM). K. A. M. Junaid [4]: uses the back propagation neural network for identification of different types of tumor like cancerous or benignant. This algorithm helps in training the neural network to do work properly in finding the tumor.…”

“…The 3rd section is about the GLCM features with RBFNN which are used to extract the features. The 4 th section explains about the Wavelets with RBFNN. The 5 th section explains about the general steps for cancer detection.…”