Department of Mechatronics Engineering, Jeju National University, Jeju-si, Republic of Korea
Zeeshan
Department of Electronics Engineering, Gachon University, Gyeonggi-do, Seongnam-si, South Korea
Javed Ali Khan
Department of Computer Science, School of Physics, Engineering, Computer Science, University of Hertfordshire, AL10 9AB Hatfield, U.K
Eman Allogmani
Department of Computer Science, College of Computer and Information Sciences, Majmaah University, Al-Majmaah, Saudi Arabia
Nora El Rashidy
Department of Machine Learning and Information Retrieval, Faculty of Artificial Intelligence, Kaferelshikh University, Kafr El-Shaikh, Egypt
Sobia Manzoor
Department of Healthcare Biotechnology, Atta-Ur-Rahman School of Applied Biosciences, National University of Sciences and Technology, Islamabad, Pakistan
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.
