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

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.

[1]  Michael Negnevitsky,et al.  Artificial Intelligence: A Guide to Intelligent Systems , 2001 .

[2]  P. Hall,et al.  Ethnic differences in the time trend of female breast cancer incidence: Singapore, 1968 – 2002 , 2006, BMC Cancer.

[3]  L. Bassett,et al.  Automated and hand-held breast US: effect on patient management. , 1987, Radiology.

[4]  Siti Nor Zawani Ahmmad,et al.  Measurement of ultrasound attenuation for normal and pathological mice breast tissue using 10MHz ultrasound wave , 2010 .

[5]  Hee Chan Kim,et al.  Computer-aided diagnosis of solid breast nodules on ultrasound with digital image processing and artificial neural network , 2004, The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[6]  Fatimah Ibrahim,et al.  Non-invasive diagnosis of risk in dengue patients using bioelectrical impedance analysis and artificial neural network , 2010, Medical & Biological Engineering & Computing.

[7]  G Pfurtscheller,et al.  Using time-dependent neural networks for EEG classification. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[8]  Mingui Sun,et al.  The forward EEG solutions can be computed using artificial neural networks , 2000, IEEE Transactions on Biomedical Engineering.

[9]  B. McAree,et al.  Breast cancer in women under 40 years of age: a series of 57 cases from Northern Ireland. , 2010, Breast.

[10]  Paul Fieguth,et al.  Neuro-fuzzy network for the classification of buried pipe defects , 2006 .

[11]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[12]  Fatimah Binti Ibrahim.  Fatimah Ibrahim, 2005, Prognosis of dengue fever and dengue haemorrhagic fever using bioelectric impedance, Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur , 2005 .

[13]  E. Conant,et al.  Interreader variability and predictive value of US descriptions of solid breast masses: pilot study. , 2001, Academic radiology.

[14]  E. Conant,et al.  A Review of Breast Ultrasound , 2006, Journal of Mammary Gland Biology and Neoplasia.

[15]  S. Hariharan,et al.  Trends in breast cancer mortality in Trinidad and Tobago--a 35-year study. , 2010, Cancer epidemiology.

[16]  L. Landini,et al.  Evaluation of the attenuation coefficients in normal and pathological breast tissue , 1986, Medical and Biological Engineering and Computing.

[17]  J. Wei,et al.  Understanding artificial neural networks and exploring their potential applications for the practicing urologist. , 1998, Urology.

[18]  Eko Supriyanto,et al.  Quantitative analysis of hybrid magnetoacoustic method for detection of normal and pathological breast tissue , 2010 .

[19]  Richard L Lammers,et al.  Prediction of traumatic wound infection with a neural network-derived decision model. , 2003, The American journal of emergency medicine.

[20]  Elif Derya íbeyli.  Implementing automated diagnostic systems for breast cancer detection , 2007 .

[21]  Abdulhamit Subasi,et al.  Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients , 2005, Expert Syst. Appl..

[22]  H. Verkooijen,et al.  Breast cancer in a multi-ethnic Asian setting: results from the Singapore-Malaysia hospital-based breast cancer registry. , 2011, Breast.

[23]  Gregg E. Trahey,et al.  Optimizing visualization of breast microcalcifications , 2000, 2000 IEEE Ultrasonics Symposium. Proceedings. An International Symposium (Cat. No.00CH37121).

[24]  P. Edmonds,et al.  Ultrasonic Tissue Characterization for Breast Biopsy Specimens , 1987, IEEE 1987 Ultrasonics Symposium.

[25]  E. Supriyanto,et al.  Hybrid magnetoacoustic method for breast tumor detection: an in-vivo and in-vitro modelling and analysis , 2010 .

[26]  Jinha M. Park,et al.  Promising techniques for breast cancer detection, diagnosis, and staging using non-ionizing radiation imaging techniques. , 2006, Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics.

[27]  F. Bray,et al.  Cancer burden in the year 2000. The global picture. , 2001, European journal of cancer.

[28]  H. Brenner,et al.  Cumulative incidence trends of selected cancer sites in a Philippine population from 1983 to 2002: a joinpoint analysis , 2010, British Journal of Cancer.

[29]  M. Buscema,et al.  Introduction to artificial neural networks. , 2007, European journal of gastroenterology & hepatology.

[30]  J. V. van Engelshoven,et al.  The role of ultrasonography as an adjunct to mammography in the detection of breast cancer. a systematic review. , 2002, European journal of cancer.

[31]  F. Ibrahim,et al.  Prognosis of Dengue Fever and Dengue Haemorrhagic Fever using Bioelectrical Impedance , 2005 .