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Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Authors:
Manish Tiwari, Prasun Chakrabarti
10.5120/ijca2016908044

Manish Tiwari and Prasun Chakrabarti. Article: Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective. International Journal of Computer Applications 133(12):31-35, January 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Manish Tiwari and Prasun Chakrabarti},
	title = {Article: Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {133},
	number = {12},
	pages = {31-35},
	month = {January},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

The authors have proposed some discovered facts related to liver cancer diagnosis .The analysis of intensity of liver cancer growth can be governed by artificial neural modeling. The progress of treatment of liver cancer based on past event (intensities of cancer growth at specific observed timing instants) can be computed on the basis of neuro-associator. The augmentation or expansion of features indicating liver cancer growth can be quantified and realized based on Markov property based state transition. The investigation related to liver cancer detection can be governed by the fundamental concept of geometric distribution. The investigation related to significance of parameters responsible for liver cancer can be realized in the light of the Cobb-Douglas model. Liver cancer detection can be realized based upon the fundamental principle of information gain. The reliability and mean time to failure of liver cancer testing system can be realized in the light of parallel system configuration. The effect of alcohol consumption leading to liver cancer can be sensed using the concept learning approach.

References

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Keywords

neuro-associator, Markov property based transition, Cobb-Douglas model, information gain, reliability, concept learning.