Artificial Neural Network based Diagnosis System

International Journal of Computer Trends and Technology (IJCTT)          
© 2017 by IJCTT Journal
Volume-48 Number-4
Year of Publication : 2017
Authors : Arti Rana, Arvind Singh Rawat, Himanshu Bahuguna, Anchit Bijalwan
DOI :  10.14445/22312803/IJCTT-V48P134


Arti Rana, Arvind Singh Rawat, Himanshu Bahuguna, Anchit Bijalwan "Artificial Neural Network based Diagnosis System". International Journal of Computer Trends and Technology (IJCTT) V48(4):189-191, June 2017. ISSN:2231-2803. Published by Seventh Sense Research Group.

Abstract -
An Artificial Neural Network (ANN) is sequence processing concept that is stimulated by the way genetic nervous systems, i.e., brain, progression information. The key element of this concept is the novel formation of the information processing system. It consists of a huge amount of highly interrelated processing elements (neurons) working in unity to resolve particular problems. ANNs, like individuals, learn by example. An ANN is configured for a particular purpose, such as data classification or pattern recognition, via learning process. Learning in genetic systems engrosses modification to the synaptic connections that exist in between the neurons. This is factual of ANNs as well. ANN is a branch of Artificial intelligence in computer science. Neural Networks are presently a popular research field in medical, specifically in the areas of cardiology, urology, radiology, oncology and etc. In this paper, an effort has been made to construct use of neural networks in the medical field.

[1] Bradshaw, J.A., Carden, K.J., Riordan, D., 1991. Ecological ?Applications Using a Novel Expert System Shell. Comp. Appl. Biosci. 7, 79–83.
[2] Lippmann, R.P., 1987. An introduction to computing with neural nets. IEEE Accost. Speech Signal Process. Mag., April: 4-22.
[3] N. Murata, S. Yoshizawa, and S. Amari, ?Learning curves, model selection and complexity of neural networks, in Advances in Neural Information Processing Systems 5, S. Jose Hanson, J. D. Cowan, and C. Lee Giles, ed. San Mateo, CA: Morgan Kaufmann, 1993, pp. 607-614.
[4] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-Based Learning Applied to Document Recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[5] M.D. Garris, C.L. Wilson, and J.L. Blue, “Neural Network-Based Systems for Handprint OCR Applications,” IEEE Trans. Image Processing, vol. 7, no. 8, pp. 1097-1112, 1998.
[6] ] D.E. Rumelhart, G.E. Hinton, and R.J. Williams, “Learning Internal Representations by Error Propagation,” Parallel Distributed Processing, D.E. Rumelhart and J.L. McClelland, eds., vol. 1, ch. 8, pp. 318-362, 1986.
[7] P. Frasconi, M. Gori, and A. Sperduti, “A General Framework for Adaptive Processing of Data Structures,” IEEE Trans. Neural Networks, vol. 9, no. 5, pp. 768-786, 1998.
[8] P. Frasconi, M. Gori, and A. Sperduti, “Guest Editors’ Introduction: Special Section on Connectionist Models for Learning in Structured Domains,” IEEE Trans. Knowledge and Data Eng., vol. 13, no. 2, pp. 145-147, Mar./Apr. 2001.
[9] M. Gori, S. Marinai, and G. Soda, “Artificial Neural Networks for Document Analysis and Recognition,” Technical Report N.1/2003, Univ. of Florence,, 2003.
[10] Z. Chi and K.W. Wong, “A Two-Stage Binarization Approach for Document Images,” Proc. Int’l Symp. Intelligent Multimedia, Video and Speech Processing (ISIMP ’01), pp. 275-278, 2001.
[11] A.P. Whichello and H. Yan, “Linking Broken Character Borders with Variable Sized Masks to Improve Recognition,” Pattern Recognition, vol. 29, no. 8, pp. 1429-1435, 1996.

ANN (Artificial Neural Network), Neurons, Diagnosis system.