Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma

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4
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6

초록

Viral and non-viral hepatocellular carcinoma (HCC) is becoming predominant in developing countries. A major issue linked to HCC-related mortality rate is the late diagnosis of cancer development. Although traditional approaches to diagnosing HCC have become gold-standard, there remain several limitations due to which the confirmation of cancer progression takes a longer period. The recent emergence of artificial intelligence tools with the capacity to analyze biomedical datasets is assisting traditional diagnostic approaches for early diagnosis with certainty. Here we present a review of traditional HCC diagnostic approaches versus the use of artificial intelligence (Machine Learning and Deep Learning) for HCC diagnosis. The overview of the cancer-related databases along with the use of AI in histopathology, radiology, biomarker, and electronic health records (EHRs) based HCC diagnosis is given.

키워드

Tumors; viral cancers; artificial intelligence; cancer diagnosis; traditional cancer diagnostic; Hepatocellular carcinoma (HCC); LIVER-TUMOR DETECTION; HEPATITIS-B-VIRUS; TEXTURE ANALYSIS; HEALTH-CARE; CLASSIFICATION; IMAGES; EPIDEMIOLOGY; SURVEILLANCE; RADIOMICS; ANALYTICS
제목
Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma
저자
Asif, Arun; Ahmed, Faheem; Zeeshan; Khan, Javed Ali; Allogmani, Eman; El Rashidy, Nora; Manzoor, Sobia; Anwar, Muhammad Shahid
DOI
10.1109/ACCESS.2024.3369491
발행일
2024-02
유형
Article
저널명
IEEE Access
권
12
페이지
37557 ~ 37571