Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinomaopen access
- Authors
- Asif, Arun; Ahmed, Faheem; Zeeshan; Khan, Javed Ali; Allogmani, Eman; El Rashidy, Nora; Manzoor, Sobia; Anwar, Muhammad Shahid
- Issue Date
- Feb-2024
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Tumors; viral cancers; artificial intelligence; cancer diagnosis; traditional cancer diagnostic; Hepatocellular carcinoma (HCC)
- Citation
- IEEE ACCESS, v.12, pp 37557 - 37571
- Pages
- 15
- Journal Title
- IEEE ACCESS
- Volume
- 12
- Start Page
- 37557
- End Page
- 37571
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/91174
- DOI
- 10.1109/ACCESS.2024.3369491
- ISSN
- 2169-3536
- Abstract
- 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.
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