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  Published Paper Details:

  Paper Title

IDENTIFICATION FOR LIVER FIBROSIS BASED ON CHRONIC HBV USING IMAGE INFERENCE

  Authors

  Soniya V Langote,  Ms.Shilpa B.Kodil

  Keywords

Key words: Non-invasive assessment; Serum- markers machine learning; image interfering, liver disease severity;

  Abstract


Liver fibrosis is the formation of abnormally large amount of tissue in the liver. It occurs when the liver attempts to repair and replace damaged cells. Non-invasive assessment of severity of liver fibrosis is crucial for understanding histology and making decisions on antiviral treatment for chronic HBV [Hepatitis B virus] in view of associated risk of biopsy. The evaluation of relation between serum markers based on ultra sound images. It will detect accuracy and shows staging of liver fibrosis. To avoid the drawbacks of liver biopsy non-invasive method using machine-learning techniques was developed recently. In this paper image, information is also included in the dataset. For image information, the new algorithm was used that is ANFIS (Adaptive Neuro Fuzzy Inference System). In this concept, an approach has been taken to predict liver grades & what effects its severity.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2112468

  Paper ID - 214310

  Page Number(s) - e408-e411

  Pubished in - Volume 9 | Issue 12 | December 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Soniya V Langote,  Ms.Shilpa B.Kodil,   "IDENTIFICATION FOR LIVER FIBROSIS BASED ON CHRONIC HBV USING IMAGE INFERENCE", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 12, pp.e408-e411, December 2021, Available at :http://www.ijcrt.org/papers/IJCRT2112468.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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