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For: Hashem S, ElHefnawi M, Habashy S, El-Adawy M, Esmat G, Elakel W, Abdelazziz AO, Nabeel MM, Abdelmaksoud AH, Elbaz TM, Shousha HI. Machine Learning Prediction Models for Diagnosing Hepatocellular Carcinoma with HCV-related Chronic Liver Disease. Comput Methods Programs Biomed 2020;196:105551. [PMID: 32580053 DOI: 10.1016/j.cmpb.2020.105551] [Cited by in Crossref: 6] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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1 Feng X, Chen X, Dong C, Liu Y, Liu Z, Ding R, Huang Q. Multi-scale information with attention integration for classification of liver fibrosis in B-mode US image. Comput Methods Programs Biomed 2021;215:106598. [PMID: 34986432 DOI: 10.1016/j.cmpb.2021.106598] [Reference Citation Analysis]
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3 Christou CD, Tsoulfas G. Role of three-dimensional printing and artificial intelligence in the management of hepatocellular carcinoma: Challenges and opportunities. World J Gastrointest Oncol 2022; 14(4): 765-793 [DOI: 10.4251/wjgo.v14.i4.765] [Reference Citation Analysis]
4 Hashem A, Awad A, Shousha H, Alakel W, Salama A, Awad T, Mabrouk M. Validation of a machine learning approach using FIB-4 and APRI scores assessed by the metavir scoring system: A cohort study. Arab J Gastroenterol 2021;22:88-92. [PMID: 33985905 DOI: 10.1016/j.ajg.2021.04.003] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 1.0] [Reference Citation Analysis]
5 Gupta S, Gupta MK. A comprehensive data‐level investigation of cancer diagnosis on imbalanced data. Computational Intelligence. [DOI: 10.1111/coin.12452] [Cited by in Crossref: 7] [Cited by in F6Publishing: 2] [Article Influence: 7.0] [Reference Citation Analysis]
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