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

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

Shankho Subhra Pal (Veer Surendra Sai University of Technology (VSSUT), Burla, India)
Copyright: © 2018 |Pages: 9
DOI: 10.4018/IJAIE.2018070102

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.
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Introduction

Breast cancer is the most common invasive cancer in females worldwide and is major cause of deaths. Experts claim that there exists no definite way to know and say why one person develops the disease while another does not. We are only aware of certain risk factors that can impact the odds of being diagnosed with one. The ability to detect early is the only way to treat breast cancer. Appropriate tests are needed to differentiate benign tumor from malignant one. The most common tests includeMammograms, Breast Ultrasound, Magnetic resonance Imaging (MRI), Ductogram and Biopsy. Biopsy is best and only way to know if the breast tumor is cancerous.Here a small sample is taken from suspected area and is test in lab. And positive detection of breast cancer through biopsy canreach as low as 10%. Many statistical techniques and cognitive science approaches like Artificial Intelligence are lately being used to detect the “malignant” group in a patient. Breast Cancer Diagnosis (BCD) problem is labelled as a classification problem. Researchers from various fields are drawn to study this problem and are using Machine Learning algorithms, Artificial Neural Networks (ANN), Support Vector Machines (SVM) and other data mining techniques. ANN, SVM, and PNNhave become effective alternatives in modeling classification problems due to their capability to capture complex nonlinear relationships among variables. Recently, functional networks have been introduced as a very effective scheme for the statistical pattern recognition problems and nonlinear complex prediction. Machine learning can be applied to get a cheap and reliable diagnosis. One of the methods of machine learning is Feed Forward Artificial Neural Network (FFANN). The FFANN can be used to classify them into malignant and benign. The objective of this paper is to classify and diagnose breast cancer.

We have used various parameters of breast cancer, like: Clump Thickness, Uniformity of Cell Size, Uniformity of Cell Shape, Marginal Adhesion, Single Epithelial Cell Size, Bare Nuclei, Bland Chromatin, Normal Nucleoli and Mitoses are used for predicting the presence of breast cancer in a given sample.

In this paper, feed forward artificial neural network has been used. To optimise the weights of the model, Grey Wolf Optimization Algorithm is used due to its proven advantages and application in various optimization problems reported in the litterateur. In section 2 Feed Forward Artificial Neural Network is discussed. Grey Wolf Optimization is discussed in section 3. In section 4 presents the result and discussion. In section 5 comparisons with different published results are given. Finally, Section 6 concludes the work.

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