An automated breast cancer diagnosis using feature selection and parameter optimization in ANN

Abstract

Detecting and treating breast cancer at earlier stages is highly proved to improve the survival rate of breast cancer patients as breast cancer is considered a major cause of death worldwide. Classical methods for diagnosing breast cancer depend on human expertise and they incur huge amounts of labor, time and are subject to human error. An Integrated Artificial Immune system and Artificial Bee Colony based breast cancer diagnosis (IAIS-ABC-CDS) is proposed for parallel processing of effective feature selection and parameter optimization in an Artificial Neural Network (ANN). The IAIS-ABC-CDS with Momentum-based Gradient Descent Backpropagation (MBGD) that uses the advantages of Simulated Annealing (SA) for enhancing local search process is compared to the benchmark diagnosis schemes of IAIS-ABC-CDS with Resilient Back-Propagation Techniques (RBPT) and Genetic Algorithm based ANN with Multilayer Perceptron (GA-ANN-MLP) schemes. The proposed IAIS-ABC-CDS is confirmed to produce a mean classification of 99.34% and 99.11% in ANN under the Wisconsin dataset.