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

Arun Asif, Faheem Ahmed, Zeeshan, Javed Ali Khan, Eman Allogmani, Nora El Rashidy, Sobia Manzoor, Muhammad Shahid Anwar

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)37557-37571
Number of pages15
JournalIEEE Access
Early online date23 Feb 2024
Publication statusE-pub ahead of print - 23 Feb 2024


  • artificial intelligence
  • cancer diagnosis
  • Hepatocellular carcinoma (HCC)
  • traditional cancer diagnostic
  • viral cancers


Dive into the research topics of 'Machine Learning Based Diagnostic Paradigm in Viral and Non-Viral Hepatocellular Carcinoma'. Together they form a unique fingerprint.

Cite this