BIONET: An artificial neural network model for diagnosis of diseases

Pattern Recognition Letters

Volumn 21, Issue 8, 2000, Pages 721-740

  Thamarai Selvi, S ^a   Arumugam, S ^b   Ganesan, L ^c  

^a MANONMANIAM SUNDARANAR UNIVERSITY   (India)

^b GOVERNMENT COLLEGE OF ENGINEERING   (India)

^c Government College of Engineering   (India)

Author keywords

Backpropagation learning; Delta rule; Diagnosis of diseases; Hard learning problem; Multilayer feedforward neural network model; Neck and arm pain diseases; Parity checking; XOR problem

Indexed keywords

BACKPROPAGATION; COMPUTER AIDED SOFTWARE ENGINEERING; DISEASES; FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; PROBLEM SOLVING;

BACKPROPAGATION LEARNING; DELTA RULE; HARD LEARNING PROBLEM; PARITY CHECKING; SOFTWARE PACKAGE BIONET;

COMPUTER AIDED DIAGNOSIS;

EID: 0033715540     PISSN: 01678655     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0167-8655(00)00027-1     Document Type: Article

References (25)

1. Abel E.W., Zacharia P.C., Forster A.et al. Neural network analysis of the EMG interference pattern. Med. Eng. Phys. 18(1):1997;12-17.
2. Arthur Guyton, C., 1991. Text Book of Medical Physiology, eighth ed., Prism Books, Bangalore, pp. 636-640.
3. Dettmar H.P., Barbour G.S., Blackwell K.T.et al. Orange juice classification with a biologically based neural network. Comput. Chem. 20(2):1996;261-266.
4. Erich, H., 1983. Order and chaos in neural systems: an approach to the dynamics of higher brain functions. IEEE Trans. Systems, Man and Cybernetics, SMC-13 (5), 782.
5. Guterman H., Nehmadi Y., Chistyakov A.et al. A comparison of neural network and Bayes recognition approaches in the evaluation of the brainsteam trigeminal evoked potentials in multiple sclerosis. Int. J. Biomed. Comput. 43(3):1997;203-213.
6. Hornik, et al., K., 1989. Multilayer feedforward networks are universal approximators. Neural Networks 2, 359-366.
7. Kandaswamy A., Kumar A. Role of medical expert systems in health care in the Indian context. Def. Sci. J. 47(4):1997;499-504.
8. Kenneth, K.N., 1985. Neck Pain. Text Book of Rheumatology, second ed. Saunders Publications, pp. 416-435.
9. Kwak N.K., Lee C. A neural network application to classification of health status of HIV/AIDS patients. J. Med. Syst. 21(2):1997;87-97.
10. Mountcastle V.B. The problem of sensing and the neural coding of sensory events. Quarton G.C., Melnechuk T., Schmitt F.O. The Neurosciences: A Study Program. 1967;327-347 The Rockefeller University Press, New York.
11. Pfurtscheller G., Kalcher J., Neuper C.et al. On-line EEG classification during externally paced hand movements using a neural network-based classifier. Electroncephalogr. Clin. Neurophysiol. 99(5):1996;416-425.
12. Ramamurthy, S., 1986. Cervical pain. In: Prithivi Raj, P. (Ed.), Practical Management of Pain, Yearbook. Medical Publishers, pp. 418-442.
13. Rumelheart, D.E., Hinton, G.E., Williams, R.J., 1986a. Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1. MIT Press, Cambridge, MA, pp. 318-362.
14. Schalkoff, R.J., 1997. Artificial Neural Networks. McGraw-Hill, New York, pp 1-12, 151-155.
15. Smith J.H., Graham J., Taylor R.J. The application of an artificial neural network to Doppler ultrasound waveforms for the classification of arterial disease. Int. J. Clin. Monit. Comput. 13(2):1996;85-91.
16. Sutter J.M., Jurs P.C. Neural network classification and quantification of organic vapors based on fluorescence data from a fibre-optic sensor array. Anal. Chem. 69(5):1997;856-862.
17. Tarun K. Foundations of Neural Networks. 1990;9-11 Addison-Wesley, Reading, MA.
18. Thamarai Selvi, S., Arumugam, S., 1998a. BIONET as an universal approximator. In: Proceedings of the Thirteenth Annual Conference of Ramanujan Mathematical Society, M.S. University, Tirunelveli, India, pp. 34-35.
19. Thamarai Selvi, S., Ramar, S. 1997. Study of signs and symptoms for neck and arm pain and input design for neural network. Technical report submitted for UGC Minor Research Project, New Delhi, pp. 56-59.
20. Thamarai Selvi, S., Ramar, S., Arumugam, S., 1995a. Connectionist expert system for neck and arm pain diagnosis. In: Proceedings of the International Conference on Automation, Shri G.S. Institue of Technology and Science, Indore, India, pp. 5-7.
21. Thamarai Selvi, S., Ramar, S., Arumugam, S., 1995b. Neck and arm pain evaluation using neural network. In: Proceedings of the Nineteenth National Systems Conference on Recent Trends in Applied systems Research, PSG College of Technology, Coimbatore, India, pp. 532-536.
22. Thamarai Selvi, S., Ramar, S., Arumugam, S., 1998b. Logical functioning of BIONET. In the National Seminar on Artificial Neural Networks and Cognitive Sciences (ANCS '98) conducted at Cochin University of Science and Technology, Cochin, 23-25 September 1998.
23. Tuulik V., Raja A., Meister A.et al. Neural network method to determine the vigilance levels of the central nervous system, related to occupational chronic chemical stress. Technol. Health Care. 5(3):1997;243-251.
24. Viktor H.L., Cloete I., Beyers N. Extraction of rules for tuberculosis diagnosis using an artificial neural network. Methods Inf. Med. 36(2):1997;160-162.
25. Zurada, J.M., 1997. Introduction to Artificial Neural Systems. Jaico Publishing House, Delhi, pp. 2226-2229.