Grey Wolf Optimization Trained Feed Foreword Neural Network for Breast Cancer Classification

Abstract

Breast cancer is the most common invasive cancer in females worldwide and is major cause of deaths. The diagnoses of breast cancer include mammograms, breast ultrasound, magnetic resonance imaging (MRI), ductogram and biopsy. Biopsy is best and only way to know if the breast tumor is cancerous. Report says that positive detection of breast cancer through biopsy can reach as low as 10%. So many statistical techniques and cognitive science approaches like artificial intelligence are used to detect the type of breast cancer in a patient for getting more accuracy. This article presents the breast cancer classification using feed foreword neural network trained by grey wolf optimization algorithm. The superiority of the GWO-FFNN is shown by experimenting Wisconsin Hospital data set (Breast Cancer Wisconsin) and comparing recently reported results. The evaluations show that the proposed approach is very robust, effective and gives better correct classification as compared to other classifiers.

A Grey Wolf-Based Method for Mammographic Mass Classification

“…Pal [15] used a grey wolf optimization (GWO) algorithm to train feed foreword neural network (FFNN), which is then used for mammogram breast cancer classification. The proposed GWO-FFNN method showed promising results when evaluated using the Wisconsin Hospital Breast Cancer dataset.…”