Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification-Reference-Cited by-同舟云学术

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Early detection of liver disorders using hybrid soft computing techniques for optimal feature selection and classification

Published:2022-12-28 Issue:6 Volume:35 Page:1-1
ISSN:1532-0626
Container-title:Concurrency and Computation: Practice and Experience
language:en
Short-container-title:Concurrency and Computation

Author:

Varchagall Manjunath¹^ORCID,Adaguru Yogegowda Prasad²^ORCID

Affiliation:

1. School of computing and information technology REVA University Bangalore India

2. Computer Science and Engineering SJB Institute of Technology Bangalore India

Publisher

Wiley

Subject

Computational Theory and Mathematics,Computer Networks and Communications,Computer Science Applications,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cpe.7576

Reference35 articles.

1. The prognostic significance of serum gamma‐glutamyltransferase levels and AST/ALT in primary hepatic carcinoma;Zhang L‐x;BMC Cancer,2019

2. Acute-on-chronic liver failure: consensus recommendations of the Asian Pacific association for the study of the liver (APASL): an update

3. An inverse stage‐shift model to estimate the excess mortality and health economic impact of delayed access to cancer services due to the COVID‐19 pandemic

4. A new definition for metabolic dysfunction-associated fatty liver disease: An international expert consensus statement

5. Feasibility of Quantitative Magnetic Resonance Fingerprinting in Ovarian Tumors for T1 and T2 Mapping in a PET/MR Setting

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