Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions

Manning, Timmy; Walsh, Paul

doi:10.1007/978-3-642-37189-9_15

Cited by 10 publications

(2 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, if one algorithm is not efficient enough, then ensemble techniques are used to improve the accuracy and make the algorithm more effective [ 5 ].Chen et al applied the rough set-support vector machine (RS-SVM) and divided datasets in 70%-30% and 50%-50% using a 5-fold cross-validation technique during experimentation [ 6 ]. Osman, A.H., has used a two-step SVM technique in which the first step is the clustering technique which is used to find the hidden pattern and SVM is used for classification [ 7 ]. As the application of AI and ML rise for the diagnosis of diseases, it can improve the efficiency and accuracy of the breast cancer diagnosis.…”

Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

Singh

Jangir²,

Kumar

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

Growth of malignant tumors in the breast results in breast cancer. It is a cause of death of many women across the world. As a part of treatment, a woman might have to go through painful surgery and chemotherapy that may further lead to severe side effects. However, it is possible to cure it if it is diagnosed in the initial stage. Recently, many researchers have leveraged machine learning (ML) techniques to classify breast cancer. However, these methods are computationally expensive and prone to the overfitting problem. A simple single-layer neural network, i.e., functional link artificial neural network (FLANN), is proposed to overcome this problem. Further, the F-score is used to reduce the issue of overfitting by selecting features having a higher significance level. In this paper, FLANN is proposed to classify breast cancer using Wisconsin Breast Cancer Dataset (WBCD) (with 699 samples) and Wisconsin Diagnostic Breast Cancer (WDBC) (with 569 samples) datasets. Experimental results reveal that the proposed models can diagnose breast cancer with higher performance. The proposed model can be used in the early breast cancer diagnosis with 99.41% accuracy.

show abstract

Section: Introductionmentioning

confidence: 99%

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

Singh

Jangir²,

Kumar

et al. 2022

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…A simple but interesting application of this method has been used by Cartesian Genetic Programming of Artificial Neural Networks (CGPANN) [55], where the widths of Gaussian functions are optimized for each neuron. That is, the genes representing the functions of the hidden neurons are re-purposed to specify the standard deviation for Gaussian functions.…”

Section: A Self-adaptive Afmentioning

confidence: 99%

EvoDNN - An Evolutionary Deep Neural Network with Heterogeneous Activation Functions

Cui

Shabash

Wiese

2019

2019 IEEE Congress on Evolutionary Computation (CEC)

View full text Add to dashboard Cite

Many problems in Computational Biology and Bioinformatics involve classification, such as the classification of cell samples into malignant (cancer) or benign (normal). For such tasks, we propose EvoDNN, an evolutionary deep neural network that employs an evolutionary algorithm to evolve deep heterogeneous feed-forward neural networks. While the majority of current feed-forward neural networks employ user defined homogeneous activation functions, EvoDNN creates heterogeneous multi-layer networks where each neuron's activation function is not statically defined by the user, but dynamically optimized during evolution. The main advantage offered by EvoDNN lies in that the activation functions do not need to be differentiable. This feature gives users a great degree of flexibility over which activation functions EvoDNN can utilize. This thesis demonstrates how EvoDNN can simultaneously optimize each neuron's weight, bias, and activation function, and empirically shows a superior performance compared to feed-forward neural networks trained with backpropagation method, random forest method, and our earlier approach EvoNN which employed a single hidden layer.

show abstract

Evolving Node Transfer Functions in Artificial Neural Networks for Handwritten Digits Recognition

Vodianyk

Rokita

2016

Computer Vision and Graphics

View full text Add to dashboard Cite

Improving the Performance of CGPANN for Breast Cancer Diagnosis Using Crossover and Radial Basis Functions

Cited by 10 publications

References 19 publications

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

[Retracted] Feature Importance Score‐Based Functional Link Artificial Neural Networks for Breast Cancer Classification

EvoDNN - An Evolutionary Deep Neural Network with Heterogeneous Activation Functions

Evolving Node Transfer Functions in Artificial Neural Networks for Handwritten Digits Recognition

Contact Info

Product

Resources

About