Diagnosis of breast cancer using hybrid magnetoacoustic method and artificial neural network

  • Authors:

  • Maheza Irna Mohamad Salim;Abd Hamid Ahmad;Ismail Ariffin;Bustanur Rosidi;Eko Supriyanto

  • Affiliations:

  • Department of Clinical Engineering, Faculty of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor, Malaysia;Department of Electronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia;Department of Electronics Engineering, Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia;Institute of Bioproduct Development, Universiti Teknologi Malaysia, Johor, Malaysia;Department of Clinical Engineering, Faculty of Biomedical Engineering and Health Sciences, Universiti Teknologi Malaysia, Johor, Malaysia

  • Venue:
  • ACS'11 Proceedings of the 11th WSEAS international conference on Applied computer science
  • Year:
  • 2011

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Abstract

This paper presents a new approach in breast cancer diagnosis by using Hybrid Magnetoacoustic Method (HMM) and artificial neural network. HMM is a newly developed one dimensional imaging system that combines the theory of acoustic and magnetism for breast imaging. It is capable to produce 2 outputs, the attenuation scale of ultrasound and the magnetoacoustic voltage. In this study, an artificial neural network was developed to automate the output of HMM for breast cancer classification. The ANN employs the steepest gradient descent with momentum back propagation algorithm with logsig and purelin transfer function. The best ANN architecture of 3-2-1 (3 network inputs, 2 neurons in the hidden layer, one network output) with learning rate of 0.3, iteration rate of 20000 and momentum constant of 0.3 was successfully developed with accuracy of 90.94% to testing data and 90% to validation data. The result shows the advantages of HMM outputs in providing a combination of bioelectric and acoustic information of tissue for breast cancer diagnosis consideration. The system's high percentage of accuracy shows that the output of HMM is very useful in assisting diagnosis. This additional capability is hoped to improve the existing breast oncology diagnosis.