CFP last date
20 March 2024
Reseach Article

Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective

by Manish Tiwari, Prasun Chakrabarti
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 133 - Number 12
Year of Publication: 2016
Authors: Manish Tiwari, Prasun Chakrabarti
10.5120/ijca2016908044

Manish Tiwari, Prasun Chakrabarti . Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective. International Journal of Computer Applications. 133, 12 ( January 2016), 31-35. DOI=10.5120/ijca2016908044

@article{ 10.5120/ijca2016908044,
author = { Manish Tiwari, Prasun Chakrabarti },
title = { Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective },
journal = { International Journal of Computer Applications },
issue_date = { January 2016 },
volume = { 133 },
number = { 12 },
month = { January },
year = { 2016 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume133/number12/23839-2016908044/ },
doi = { 10.5120/ijca2016908044 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:31:00.242023+05:30
%A Manish Tiwari
%A Prasun Chakrabarti
%T Discovered Facts Related to Liver Cancer Diagnosis – A Research Perspective
%J International Journal of Computer Applications
%@ 0975-8887
%V 133
%N 12
%P 31-35
%D 2016
%I 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
  1. Bharathi, A., Natarajan, A. M. , “Efficient Classification of Cancer using Support Vector Machines and Modified Extreme Learning Machine based on Analysis of Variance Features”, American Journal of Applied Sciences, 8(12), pp.1295-1301,2011.
  2. Feitelson MA, Sun B, SatirogluTufan NL, Liu J, Pan J, Lian Z., “Genetic mechanisms of hepatocarcinogenesis”. Oncogene,21,pp.2593–2604,2002.
  3. Wong BW, Luk JM, Ng IO, Hu MY, Liu KD, Fan ST., “Identification of liver intestine cadherin in hepatocellular carcinoma--a potential disease marker” ,BiochemBiophys Res Commun., 311(3),pp. 618-624,2003.
  4. Ms.Vincey Jeba Malar, V. , “Computer Aided Diagnosis for liver Cancer Feature Extraction”, The International Journal Of Engineering And. Science ,2(11), pp. 27-30,2013.
  5. National Health Service, http://www.Cancer research uk.org.
  6. World Health Organization, Liver cancer report, http://www.cancer researchuk.org.
  7. Cancer Research UK, liver cancer causes, signs and symptoms, http://www.cancer researchuk.org.
  8. National Cancer Institute, http://www. cancer.gov /cancer topics.
Index Terms

Computer Science
Information Sciences

Keywords

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