Research Article
Bladder Cancer Diagnosis using Artificial Neural Network
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 83 - Issue 6 |
| Published: December 2013 |
| Authors: Shaymaa M. Alkashef, Abdelhameed Ibrahim, Hesham Arafat, Tarek A. El-Diasty |
10.5120/14451-2709
|
Shaymaa M. Alkashef, Abdelhameed Ibrahim, Hesham Arafat, Tarek A. El-Diasty . Bladder Cancer Diagnosis using Artificial Neural Network. International Journal of Computer Applications. 83, 6 (December 2013), 11-17. DOI=10.5120/14451-2709
@article{ 10.5120/14451-2709,
author = { Shaymaa M. Alkashef,Abdelhameed Ibrahim,Hesham Arafat,Tarek A. El-Diasty },
title = { Bladder Cancer Diagnosis using Artificial Neural Network },
journal = { International Journal of Computer Applications },
year = { 2013 },
volume = { 83 },
number = { 6 },
pages = { 11-17 },
doi = { 10.5120/14451-2709 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2013
%A Shaymaa M. Alkashef
%A Abdelhameed Ibrahim
%A Hesham Arafat
%A Tarek A. El-Diasty
%T Bladder Cancer Diagnosis using Artificial Neural Network%T
%J International Journal of Computer Applications
%V 83
%N 6
%P 11-17
%R 10.5120/14451-2709
%I Foundation of Computer Science (FCS), NY, USA
Abstract
The analysis of Magnetic Resonance Imaging (MRI) images using Artificial Neural Network (ANN)-based system is implemented in this paper to achieve a rapid and accurate diagnosis tool for bladder cancer. The proposed approach comprises image enhancement, removal of border, feature extraction and bladder cancer recognition using multilayer perception (MLP) with sequential weight/bias training function. We develop a model that defines the cancer level in order to enhance its treatment. Experimental results show that the devised approach increases the accuracy of diagnosis of bladder cancer up to 95%.
References
- S. Haykin, Neural Networks, Comprehensive Foundation. NewDehli: Prentice Hall of India, 2006.
- A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, and M. J. Thun. Cancer statistics, 2009. CA Cancer J Clin, 59(4) 2009,225–249,.
- Ries, L. A. G. , Harkins, D. , Krapcho, M. , Mariotto, A. , Miller, B. A. , Feuer, E. J. , et al. (2005, Novermber, 2005). SEER Cancer Statistics Review, 1975-2003.
- Baffa, R. , Letko, J. , McClung, C. , LeNoir, J. , Vecchione, A. , & Gomella, L. G. , Molecular genetics of bladder cancer: targets for diagnosis and therapy. J Exp Clin Cancer Res, 25(2), 2006, 145-160,
- Botteman, M. F. , Pashos, C. L. , Redaelli, A. , Laskin, B. , & Hauser, R. The health economics of bladder cancer: a comprehensive review of the published literature. Pharmacoeconomics, 21(18), 2003, 1315-1330
- Greene, F. L. , Page, D. L. , Fleming, I. D. , Fritz, A. G. , Balch, C. M. , Haller, D. G. , et al. (Eds. ). AJCC Cancer Staging Handbook (Sixth ed. ). New York: Springer, 2002. .
- Rajesh, A. , Sokhi, H. K. , Fung , R. , Mulcahy, K. A. ,and Bankart M. J. G,: "Bladder cancer: Evaluation of staging accuracy using dynamic MRI", Clinical Radiology, 66, 2011, 1140-1145,.
- Marchevsky, A. Mario," The Use of Artificial Neural Networks for the Diagnosis and Estimation of Prognosis in Cancer Patients", Chapter 9, Department of Pathology, Cedars-Sinai Medical Center, CA 90048, USA, ,2007, 243-259.
- Fuchs, T. ,: "Computational pathology: Challenges and promises for tissue analysis", Computerized Medical Imaging and Graphics 35, 2011, 515– 530,.
- Rosenkrantz, B. Andrew , Niver, E. Benjamin, Kopec, Martin , Berkman, S. Douglas, Lepor, Herbert , . Babb , S. James and Hecht, Elizabeth M. ,: "T1 hyperintensity of bladder urine at prostate MRI: frequency and comparison with urinalysis findings", Clinical Imaging 35, 2011, 203–207.
- Sakai, I. , Miyake, H. , Harada, K. , Hara, I. , Inoue, T. A. , & Fujisawa, M. Analysis of factors predicting intravesical recurrence of superficial transitional cell carcinoma of the bladder without concomitant carcinoma in situ. Int J Urol, 13(11), 2006. 1389-1392.
- G. Alizadeh, J. Frounchi, M. Baradaran Nia, S. Asgarifar, and M. H. Zarifi, "An Artificial Neural Network for Prediction of Cetane Number in Diesel Fuel Implemented on a FPGA," in Proc. IEEE International Conference on Computer and Communication Engineering, Kuala Lumpur, Malaysia, 2008. .
- Ringner, M. and Peterson, C. , : "Microarray-based cancer diagnosis with artificial neural networks", Biotechniques, 34, (Suppl 3), 2003, 30–35.
- - Yolda? O, Tez M, Karaca T. ,: "Artificial neural networks in the diagnosis of acute appendicitis". Am J Emerg Med, 7, 2012, 1245-7.
- S. Natarajan, R. Singh, M. Lee, B. Cox, M. Culjat, H. Lee, and W. Grundfest, Step-fmcw signaling and target detection for ultrasound imaging systems with conformal transducer arrays," in Proceedings of SPIE, vol. 7555, 2010. ,p. 75550M.
- Kipp, B. R. , Sebo, T. J. , Griffin, M. D. , Ihrke, J. M. , & Halling, K. C. Analysis of polyomavirus-infected renal transplant recipients' urine specimens: correlation of routine urine cytology, fluorescence in situ hybridization, and digital image analysis. Am J Clin Pathol, 124(6), 2005 , 854-861.
- I. Jolliffe. Principal Component Analysis. Springer-Verlag New York Inc. , New York, NY, 1986.
- L. D. F. Costa and R. M. Cesar, Jr. Shape Analysis and Classification: Theory and Practice. CRC Press LLC, Boca Raton, FL, 2001.
- Illinois , Y. Peng: " Computer-Aided Histological Analysis For Prostate Cancer Diagnosis", The Faculty Of The Division Of The Biological Sciences And The Pritzker School Of Medicine In Candidacy For The Degree Of Doctor Of Philosophy Committee On Medical Physics, Chicago, June 2010, P. 67
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