Abstract: Liver diseases present significant diagnostic and management challenges due to their asymptomatic progression and the limitations of traditional diagnostic methods. Machine learning (ML) and deep learning (DL) techniques have emerged as transformative tools in liver disease diagnostics, enabling improved accuracy, efficiency, and automation in tasks such as disease classification, liver segmentation, and lesion detection. This review consolidates findings from recent studies, covering the use of logistic regression, support vector machines (SVMs), and convolutional neural networks (CNNs) in analysing clinical and imaging data. Advanced models such as DenseNet, YOLOv8, and DBN-DNN have demonstrated state-of-the-art performance in lesion detection, real-time diagnosis, and segmentation, achieving accuracy rates exceeding 95% in most cases. Despite their promise, challenges such as dataset limitations, variability in imaging protocols, and model interpretability remain significant barriers to clinical adoption. Future research should focus on enhancing generalizability across imaging modalities, incorporating explainable AI (XAI), and optimizing real-time deployment. This review highlights the potential of ML to revolutionize liver disease diagnostics, bridging existing gaps and paving the way for scalable, accurate, and efficient clinical solutions.
Keywords: Machine Learning, Liver Disease, Deep Learning, Segmentation, Classification, Real-Time Diagnostics, Multi-Modal Integration.
|
DOI:
10.17148/IJARCCE.2025.14679
