THE APPLICATION OF DEEP NEURAL NETWORK FOR BREAST CANCER CLASSIFICATION

Devi Nurtiyasari, Department of Mathematic, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Indonesia
Abdurakhman Abdurakhman, Department of Mathematic, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Indonesia
Muhamad Rashif Hilmi, Department of Mathematic, Faculty of Mathematics and Natural Science, Universitas Gadjah Mada, Indonesia

Abstract


Breast cancer is one of the most common cancers, especially for women. Early detection of breast cancer may increase the survival rate of patients significantly. Detecting breast cancer from breast image can be done by classification process. There are so many classification models which can be used for the classification process, such as neural network, fuzzy, neuro fuzzy, wavelet neural network, wavelet neuro fuzzy, etc. This research propose one of the neural network variant, i.e. Deep Neural Network. This kind of neural network model is using at least two hidden layers on the network. The more hidden layers used the deeper the neural network will be. The architecture of Deep Neural Network used in this research is feedforward network. Classification of breast tumor using Deep Neural Network model provides results with sensitivity, specificity, and accuracy were respectively 100%, 100%, and 66.67% for training data and 100%, 20%, and 60% for testing data.

 

Keywords: breast cancer, Deep Neural Network


Keywords


breast cancer, Deep Neural Network

Full Text:

PDF

References


International Agency for Research on Cancer, World Cancer Report 2014, edited by Stewart, B.W. and Wild, C.P. (World Health Organization, Lyon, 2014), pp. 17-19.

Harralick, R.M. et al, Textural Feature for Image Classification. (IEEE Transaction on System, Man and Cybernetics. Vol. 3. No. 6., 1973). pp. 619.

Nielsen, M.A., Neural Networks and Deep Learning. (Determination Press, 2015).

Yeung. D.S, et al, Sensitivity Analysis for Neural Network. (Springer, Berlin, 2010). pp. 2.

Information Society Technology, The mini-MIAS Database of Mammograms. (2004)




DOI: https://doi.org/10.21831/jsd.v7i1.22237

Refbacks

  • There are currently no refbacks.


Copyright (c) 2018 Devi Nurtiyasari, Abdurakhman Abdurakhman, Muhamad Rashif Hilmi


Printed ISSN (p-ISSN): 2085-9872
Online ISSN (e-ISSN): 2443-1273

Indexer:
     

Creative Commons License
 
Jurnal Sains Dasar  is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License
 
Free counters!
 
View My Stats