Breast cancer diagnosis based on joint variable selection and Constructive Deep Neural Network

2018 ◽  
Author(s):  
R. Zemouri ◽  
N. Omri ◽  
C. Devalland ◽  
L. Arnould ◽  
B. Morello ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Variable Selection ◽  
Cancer Diagnosis ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Joint Variable

scholarly journals Constructive Deep Neural Network for Breast Cancer Diagnosis

2018 ◽  
Vol 51 (27) ◽  
pp. 98-103 ◽  
Author(s):  
R. Zemouri ◽  
N. Omri ◽  
B. Morello ◽  
C. Devalland ◽  
L. Arnould ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis

Breast cancer diagnosis using multiple activation deep neural network

2021 ◽  
pp. 1063293X2110251
Author(s):  
K Vijayakumar ◽  
Vinod J Kadam ◽  
Sudhir Kumar Sharma
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Deep Neural Network ◽  
Breast Cancer Diagnosis ◽  
Activation Function ◽  
Linear Functions ◽  
Cancer Data ◽  
Final Layer ◽  
Improved Performance ◽  
Hidden Layer

Deep Neural Network (DNN) stands for multilayered Neural Network (NN) that is capable of progressively learn the more abstract and composite representations of the raw features of the input data received, with no need for any feature engineering. They are advanced NNs having repetitious hidden layers between the initial input and the final layer. The working principle of such a standard deep classifier is based on a hierarchy formed by the composition of linear functions and a defined nonlinear Activation Function (AF). It remains uncertain (not clear) how the DNN classifier can function so well. But it is clear from many studies that within DNN, the AF choice has a notable impact on the kinetics of training and the success of tasks. In the past few years, different AFs have been formulated. The choice of AF is still an area of active study. Hence, in this study, a novel deep Feed forward NN model with four AFs has been proposed for breast cancer classification: hidden layer 1: Swish, hidden layer, 2:-LeakyReLU, hidden layer 3: ReLU, and final output layer: naturally Sigmoidal. The purpose of the study is twofold. Firstly, this study is a step toward a more profound understanding of DNN with layer-wise different AFs. Secondly, research is also aimed to explore better DNN-based systems to build predictive models for breast cancer data with improved accuracy. Therefore, the benchmark UCI dataset WDBC was used for the validation of the framework and evaluated using a ten-fold CV method and various performance indicators. Multiple simulations and outcomes of the experimentations have shown that the proposed solution performs in a better way than the Sigmoid, ReLU, and LeakyReLU and Swish activation DNN in terms of different parameters. This analysis contributes to producing an expert and precise clinical dataset classification method for breast cancer. Furthermore, the model also achieved improved performance compared to many established state-of-the-art algorithms/models.

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

2016 ◽  
pp. 203-214 ◽  
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Multilayer Perceptron ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Data Sets ◽  
Self Organizing Map ◽  
Intelligent Information ◽  
Self Organizing

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

Diagnosis of Breast Cancer Using Intelligent Information Systems Techniques

2016 ◽  
Vol 7 (1) ◽  
pp. 65-75 ◽  
Author(s):  
Ahmad Al-Khasawneh
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Cancer Diagnosis ◽  
Multilayer Perceptron ◽  
Breast Cancer Diagnosis ◽  
Machine Learning Algorithms ◽  
Small Data ◽  
Self Organizing Map ◽  
Term Survival ◽  
Self Organizing

Breast cancer is the second leading cause of cancer deaths in women worldwide. Early diagnosis of this illness can increase the chances of long-term survival of cancerous patients. To help in this aid, computerized breast cancer diagnosis systems are being developed. Machine learning algorithms and data mining techniques play a central role in the diagnosis. This paper describes neural network based approaches to breast cancer diagnosis. The aim of this research is to investigate and compare the performance of supervised and unsupervised neural networks in diagnosing breast cancer. A multilayer perceptron has been implemented as a supervised neural network and a self-organizing map as an unsupervised one. Both models were simulated using a variety of parameters and tested using several combinations of those parameters in independent experiments. It was concluded that the multilayer perceptron neural network outperforms Kohonen's self-organizing maps in diagnosing breast cancer even with small data sets.

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