Liver Disease Diagnosis Using Machine Learning: A Review of Imaging-Based Methods

Abstract

Liver disease is one of the major health threats throughout the world. Among the different imaging modalities involved in diagnosing and managing liver conditions, Magnetic resonance imaging (MRI) and computerized tomography (CT) scan play a pivotal role. Various studies were focused on developing automated systems to detect and classify liver diseases using advanced image processing and machine learning algorithms. A review of the literature shows that machine learning models are capable of predicting liver disorders from MRI and CT images. Research indicates that deep learning techniques, especially convolutional neural networks (CNNs), surpass traditional approaches in the extraction and classification of features, thereby enhancing diagnostic accuracy and facilitating early disease detection. This study advances global healthcare initiatives by employing machine learning to enhance the precision of liver disease diagnosis and treatment, aligning with (Goal 3: Good Health and Well-Being). This also advances medical technology by fostering innovation in medical imaging and incorporating AI-driven solutions into healthcare systems (Goal 9: Industry, Innovation, and Infrastructure).