Breast Cancer Diagnosis Using Multi-Stage Weight Adjustment In The MLP Neural Network. (19th August 2020)

• Record Type: Journal Article
• Authors: Rezaeipanah, Amin; Ahmadi, Gholamreza
• Is Part Of: Computer journal. Volume 65:Number 4(2022)
• Page Start: 788
• Page End: 804
• Publication Date: 2020-08-19
• Subjects: multi-stage weight adjustment -- MLP neural network -- genetic algorithm -- breast cancer diagnosis -- effective features selection; Computers -- Periodicals; 005.1
• DOI: 10.1093/comjnl/bxaa109
• Languages: English
• ISSNs: 0010-4620

Abstract

Abstract: Breast cancer is the most common kind of cancer, which is the cause of death among the women worldwide. There is evidence that shows that the early detection and treatment can increase the survival rate of patients who suffered this disease. Therefore, this paper proposes an automatic breast cancer diagnosis technique using a genetic algorithm for simultaneous feature selection and parameter optimization of an Multi Layer Perceptron (MLP) neural network. The aim of this paper is to propose a hybrid classification algorithm based on Multi-stage Weights Adjustment in the MLP (MWAMLP) neural network in two parts to improve the breast cancer diagnosis. In the first part, the three classifiers are trained simultaneously on the learning dataset. The output of the first part classifier together with the learning dataset is placed in a new dataset. This dataset uses a hybrid classifier method to model the mapping between the outputs of each ordinary classifier of the first part with real output labels. The proposed algorithm is implemented with three different variations of the backpropagation (BP) technique, namely the Levenberg–Marquardt, resilient BP and gradient descent with momentum for fine tuning of the weight of MLP neural network and their performances are compared. Interestingly, one of the proposed algorithms titled MWAMLP-RP produces the best and on average, 99.35% and 98.74% correct classification, respectively, on the Wisconsin Breast Cancer Database dataset, which is comparable with the obtained results from the methods titled GP-DLNN, GAANN and CAFS and other works found in the literature.