A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

Journal of Clinical Pathology

Volumn 58, Issue 9, 2005, Pages 932-938

  Paul, R R ^b   Mukherjee, A ^c   Dutta, P K ^d   Banerjee, S ^d   Pal, M ^e   Chatterjee, J ^f   Chaudhuri, K ^a   Mukkerjee, K ^a  

^a INDIAN INSTITUTE OF CHEMICAL BIOLOGY   (India)

^b DR R AHMED DENTAL COLLEGE AND HOSPITAL   (India)

^c TATA CONSULTANCY SERVICES   (India)

^d INDIAN INSTITUTE OF TECHNOLOGY   (India)

^e Institute of Interdisciplinary Scientific Research (IISR)   (India)

^f MEDICAL COLLEGE AND HOSPITAL   (India)

Author keywords

[No Author keywords available]

Indexed keywords

COLLAGEN FIBER;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CONTROLLED STUDY; DIAGNOSTIC PROCEDURE; HUMAN; HUMAN TISSUE; LEUKOPLAKIA; MAJOR CLINICAL STUDY; MOUTH DISEASE; PRIORITY JOURNAL; TRANSMISSION ELECTRON MICROSCOPY;

ALGORITHMS; COLLAGEN; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY, ELECTRON; MOUTH NEOPLASMS; NEOPLASM STAGING; NEURAL NETWORKS (COMPUTER); ORAL SUBMUCOUS FIBROSIS; PRECANCEROUS CONDITIONS;

EID: 24644450747     PISSN: 00219746     EISSN: None     Source Type: Journal    
DOI: 10.1136/jcp.2004.022095     Document Type: Article

References (33)

1. Parkin DM, Pisani P, Ferlay J. Estimates of the worldwide incidence of twenty five major cancers in 1990. Int J Cancer 1999;80:827-41.
2. Aziz SR. Oral submucous fibrosis: an unusual disease. J N J Dent Assoc 1997;68:17-19.
3. Canniff JP, Harvey W, Harris M. Oral submucous fibrosis: its pathogenesis and management. Br Dent J 1986;160:429-34.
4. Seedat HA, van Wyk CW. Betel-nut chewing and submucous fibrosis in Durban. S Afr Med J 1988;74:568-71.
5. Maher R, Lee AJ, Warnakulasuriya KA, et al. Role of areca nut in the causation of oral submucous fibrosis: a case-control study in Pakistan. J Oral Pathol Med 1994;23:65-9.
6. Zain RB, Ikeda N, Razak IA, et al. A national epidemiological survey of oral mucosal lesions in Malaysia. Community Dent Oral Epidemiol 1997;25:377-83.
7. Regezi JA, Sciubba JJ. Oral pathology: clinical pathologic correlations. London: WB Saunders, 1999.
8. Liao PH, Lee TL, Yang LC, et al. Adenomatous polyposis coli gene mutation and decreased wild-type p53 protein expression in oral submucous fibrosis: a preliminary investigation. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2001;92:202-7.
9. Canniff JP, Harvey W. The aetiology of oral submucous fibrosis: the stimulation of collagen synthesis by extracts of areca nut. Int J Oral Surg 1981;10(suppl 1):163-7.
10. van Wyk CW, Grobler-Rabie AF, Martell RW, et al. HLA-antigens in oral submucous fibrosis. J Oral Pathol Med 1994;23:23-7.
11. Paul RR, Chatterjee J, Das AK, et al. Altered elemental profile as indicator of homeostatic imbalance in pathogenesis of oral submucous fibrosis. Biol Trace Elem Res 2002;87:45-56.
12. Paul RR, Chatterjee J, Das AK, et al. Zinc and iron as bioindicators of precancerous nature of oral submucous fibrosis. Biol Trace Elem Res 1996;54:213-30.
13. Pindborg JJ, Sirsat SM. Oral submucous fibrosis. Oral Surg Oral Med Oral Pathol 1966;22:764-79.
14. Hardie J. Oral submucous fibrosis. A review with case reports. J Can Dent Assoc 1987;53:389-93.
15. Huang IY, Shieh TY. Collagen content and types in oral submucous fibrosis. Gaoxiong Yi Xue Ke Xue Za Zhi 1989;5:162-71.
16. Harvey W, Scutt A, Meghji S, et al. Stimulation of human buccal mucosa fibrobfasts in vitro by betel-nut alkaloids. Arch Oral Biol 1986;31:45-9.
17. Kuo MY, Chen HM, Hahn LJ, et al. Collagen biosynthesis in human oral submucous fibrosis fibroblast cultures. J Dent Res 1995;74:1783-8.
18. Shieh TY, Yang JF. Collagenase activity in oral submucous fibrosis. Proc Natl Sci Counc Repub China B 1992;16:106-10.
19. van Wyk CW, Seedat HA, Phillips VM. Collagen in submucous fibrosis: an electron microscopic study. J Oral Pathol Med 1990;19:182-7.
20. Dayhoff JE, Deleo JM. Artificial neural network-opening the black box. Cancer 2001;91:1615-35.
21. Kappen HJ, Neijt JP. Advanced ovarian cancer: neural network analysis to predict treatment outcome. Ann Oncol 1993;4(suppl 4):31-4.
22. Maclin P, Dempsey J. Using an artificial neural network to diagnose hepatic masses. J Med Syst 1992;16:215-25.
23. Ravdin PM, Clark GM. A practical application of neural network analysis for predicting outcome of individual breast cancer patients. Breast Cancer Res Treat 1992;22:285-93.
24. Wilding P, Morgan MA, Grygotis AE, et al. Application of back propagation neural networks to diagnosis of breast and ovarian cancer. Cancer Lett 1994;74:143-53.
25. Wu Y, Giger ML, Doi K, et al. Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer. Radiology 1993;187:81-7.
26. Chatterjee J, De K, Basu SK, et al. Collagen, zinc and iron contents of rat skin irradiated with chronic low-dose X-ray. Indian J Med Res 1993;98:243-7.
27. Daubechies I. Ten lectures on wavelets. Philadelphia: SIAM, 1992.
28. Vellerli M, Herley C. Wavelet and filter banks: theory and design. IEEE Trans Signal Process 1992;40:2207-32.
29. Mishra A, Dutta PK, Ghosh MK. Non-rigid cardiac motion quantification from 2-D image sequences based on wavelet synthesis. Image and Vision Computing 2001;19:929-39.
30. Withdrawn.
31. Cybenko G. Approximation by superpositions of sigmoidal functions. Mathematics of Control Signals and Systems 1989;2:303-14.
32. Demuth H, Beale M. Neural network toolbox-for use with MATLAB users guide. Massachusetts: The Mathworks Inc, 1993.
33. Fiori S, Grimani F, Burrascano P. Novel neural network feature selection procedure by generalization maximization with application to automatic robot guidance. International Journal of Smart Engineering and System Design 2002;4:91-106.