Computer Vision and Deep Learning for Liver Disease Diagnosis
A Systematic Review
DOI:
https://doi.org/10.61704/pr.544Keywords:
Computer vision (CV), Artificial intelligence (AI), Liver Disease, Machine Learning (ML), Deep Learning (DL), NAFLD, ALDAbstract
Liver diseases remain a major global health concern, posing significant diagnostic challenges due to their complex and heterogeneous nature. Conventional diagnostic methods rely heavily on clinical expertise and subjective interpretation, which can lead to variability and error. With recent advancements in artificial intelligence (AI), particularly in computer vision and deep learning (DL), there is a growing opportunity to improve the accuracy and efficiency of liver disease diagnosis. This review explores the applications of computer vision in the medical imaging analysis of liver diseases, including key processes such as image preprocessing, segmentation, lesion detection, and disease classification. The study also discusses the limitations of current techniques, critical challenges including data quality, model interpretability, and privacy, as well as emerging research directions. The integration of computer vision into clinical workflows holds great promise for enhancing early diagnosis, supporting clinical decision-making, and ultimately improving patient outcomes.
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Copyright (c) 2025 Raad W. Salah, Ielaf O. AbdulMajjed
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Copyright © 2025 by the authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). You may not alter or transform this work in any way without permission from the authors. Non-commercial use, distribution, and copying are permitted, provided that appropriate credit is given to the authors and Al-Hadba University.
