Pancreatic Cancer Detection and Diagnosis Expert System using Artificial Neural Networks and Fuzzy Logic Techniques

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Cancer is a class of diseases characterized by out-of control cell growth and pancreatic cancer (PC) occurs when this uncontrolled cell growth begins in the pancreas. If it is malignant and not detected at early stages, it may cause death. The aim of this research work is to present artificial neural network and fuzzy logic in pancreatic disease diagnosis based on a set of symptoms. The real procedure of medical diagnosis which usually is employed by physicians was analysed and converted to a machine implementable format. This paper presents an approach to detect the various stages of pancreatic cancer affected patients. Outcome suggests the effectiveness of using neural network over manual detection procedure.

Published In : IJCSN Journal Volume 5, Issue 6

Date of Publication : December 2016

Pages : 873-877

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N. V. Ramana Murty : Research Scholar, Department of Computer Science Engineering, Rayalaseema University, Kurnool, Andhra Pradesh, India.

M. S. Prasad Babu : Senior Professor, Department of CS & SE, AU College of Engineering (A), Andhra University, Visakhapatnam – 530041 , Andhra Pradesh, India.

Neural Networks , Pancreatic Cancer , Fuzzy Logic

This System presents a neural network based approach for pancreatic cancer diagnosis. Pancreatic cancer detection in its early stage is the key of its cure. The automatic diagnosis of pancreatic cancer is an important medical problem. This system uses a set of fuzzy values incorporated into neural network system is more effecter than the normal system.

[1] V. Prasath, N. Lakshmi, M. Nathiya, N. Bharathan and N. P. Neetha “A Survey on the Applications of Fuzzy Logic in Medical Diagnosis Support Systems Systems Decision,” International Journal of Scientific & Engineering Research, ISSN 2229-5518 , vol. 4, no. 4, (2013), pp. 1199-1203. [2] X. Y. Djam and Y. H. Kimbi, “A Decision Support System for Tuberculosis Diagnosis,” The Pacific Journal of Science and Technology, vol. 12, no. 2, (2011), pp. 410-425. [3] S. P. Singh and P. Johri, “A Review of Estimating Development Time and Efforts of Software Projects by Using Neural Network and Fuzzy Logic in MATLAB,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 2, no. 10, (2012), pp. 306-310. [4] P. D. C. R. Jayarathna, J. V Wijayakulasooriya and S. R. Kodituwakku, “Fuzzy Logic and Neural Network Control Systems for Backing up a Truck and a Trailer,” Int. J Latest Trends Computing, ISSN: 2045-5364, no. 3, (2011), pp. 370-377. [5] R. Malhotra, N. Singh and Y. Singh, “Genetic Algorithms : Concepts , Design for Optimization of Process Controllers,” Canadian Center of Science and Education, vol. 4, no. 2, (2011), pp. 39-54. [6] O. Taylan, “Computers & Industrial Engineering An adaptive neuro-fuzzy model for prediction of student ’s academic performance”, Computers & Industrial Engineering, Elsevier, doi:10.1016/j.cie.2009.01.019, vol. 57, (2009), pp. 732-741. [7] K. Adlassnig, “Fuzzy Set Theory in Medical Diagnosis,” IEEE Transactions on Systems, Man, and Cybernetics, (1986), pp. 260-265. International Journal of Bio-Science and Bio-Technology Vol.7, No.6 (2015) 96 Copyright ? 2015 SERSC [8] N. Walia, S. K. Tiwari and R. Malhotra, “Design and Identification of Tuberculosis using Fuzzy Based Decision Support System,” Advances in Computer Science and Information Technology, ISSN: 2393- 9915, vol. 2, no. 8, (2015), pp. 57-62. [9] M. A. S. Durai, N. C. S. N. Iyengar and A. Kannan, “Enhanced Fuzzy Rule Based Diagnostic Model for Lung Cancer using Priority Values,” International Journal of Computer science and information technologies, vol. 2, no. 2, (2011), pp. 707-710. [10] N. Annals and O. F. Natural, “Decision Support System fot the Identification of Tuberculosis using Neuro Fuzzy Logic”, Nigerian Annals of Natural Sciences, vol. 12, no. 1, (2012), pp. 12-20. [11] T. Uc, A. Karahoca and D. Karahoca, “Tuberculosis disease diagnosis by using adaptive neuro fuzzy inference system and rough sets,” Neural Comput & Applic, DOI 10.1007/s00521-012-0942-1, (2013), pp. 471-483. [12] ADLASSNIG, K.-P.; KOLARZ, G.; SCHEITHAUER, W., 1985, “Present State of the Medical Expert System CADIAG-2 [13] Michael Schuerz , Klaus-peter Adlassnig , Charles Lagor , Barbara Scheider , Georg Grabner, Definition of Fuzzy Sets Representing Medical Concepts and Acquisition of Fuzzy Relationships Between Them by Semi-Automatic Procedures [14] J. G. Vaskor, S. M. Dickinson and J. S. Bradley, “Effect of sampling on the statistical descriptors of traffic noise,” Appl. Acoust., vol.12, pp.111-124, 1979. IJCSNS International Journal of Computer Science and Network Security, VOL.11 No.4, April 2011.