Volume 7 Issue 2 - July 2016

  • 1. Breast cancer diagonsis using artifical neural network

    Authors : Kalpana Kaushik

    Pages : 41-48

    DOI : http://dx.doi.org/10.21172/1.72.507

    Keywords : Keywords—breast cancer; artificial neural networks; learning machine; gradient-based back propagation; medical decision support systems.

    Abstract :

    Artificial neural network has been widely used in various fields as an intelligent tool in recent years, such as artificial intelligence, pattern recognition, medical diagnosis, machine learning and so on. The classification of breast cancer is a medical application that poses a great challenge for researchers and scientists. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. There are parameters to be set in the beginning, long time for training process, and possibility to be trapped in local minima. Databases of breast cancer (Wisconsin Breast Cancer (WBC), Wisconsin Diagnosis Breast Cancer (WDBC) and Wisconsin Prognosis Breast Cancer (WPBC) by using classification accuracy and confusion matrix based on 10-fold cross validation method. Also, we introduce a fusion at classification level between these classifiers to get the most suitable multi-classifier approach for each data set. The experimental results show that in the classification using fusion of MLP and J48 with the PCA is superior to the other classifiers using WBC data set. Results showed that Learning Machine Neural Networks (LM ANN) has better generalization classifier model than BP ANN. However, the standard Gradient-Based Back Propagation Artificial Neural Networks (BP ANN). The development of this technique is promising as intelligent component in medical decision support systems. Recently, the neural network has become a popular tool in the classification of cancer datasets. Classification is one of the most active research and application areas of neural networks.

    Citing this Journal Article :

    Kalpana Kaushik, "Breast cancer diagonsis using artifical neural network", https://www.ijltet.org/journal_details.php?id=906&j_id=3211, Volume 7 Issue 2 - July 2016, 41-48, #ijltetorg