Application of Supervised Self-Organizing Maps in Breast Cancer Diagnosis by Total Synchronous Fluorescence Spectroscopy

2011 ◽  
Vol 65 (3) ◽  
pp. 293-297 ◽  
Author(s):  
Tatjana Dramićanin ◽  
Bogomir Dimitrijević ◽  
Miroslav D. Dramićanin
Keyword(s):  
Breast Cancer ◽  
Fluorescence Spectroscopy ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Synchronous Fluorescence ◽  
Self Organizing Maps ◽  
Synchronous Fluorescence Spectroscopy ◽  
Self Organizing

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.

Physical Activity and Survival Subsequent to Metastatic Breast Cancer Diagnosis

2010 ◽  
Author(s):  
Susan Sharp ◽  
Ashleigh Golden ◽  
Cheryl Koopman ◽  
Eric Neri ◽  
David Spiegel
Keyword(s):  
Breast Cancer ◽  
Physical Activity ◽  
Metastatic Breast Cancer ◽  
Cancer Diagnosis ◽  
Metastatic Breast ◽  
Breast Cancer Diagnosis

The significance of patient’s knowledge and behavior after breast cancer diagnosis

2019 ◽  
Vol 3 (48) ◽  
pp. 7
Author(s):  
Alina Oana Rusu-Moldovan ◽  
Maria Iuliana Gruia ◽  
Dan Mihu
Keyword(s):  
Breast Cancer ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
And Behavior
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