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

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 limita...

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Bibliographic Details
Main Authors: Arun Asif, Faheem Ahmed, Zeeshan, Javed Ali Khan, Eman Allogmani, Nora El Rashidy, Sobia Manzoor, Muhammad Shahid Anwar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
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Online Access:https://ieeexplore.ieee.org/document/10444549/
Description
Summary: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.
ISSN:2169-3536