The liver is an essential organ in the human body, and most liver diseases and lesions are often difficult to detect early due to the lack of clear symptoms. This leads to a high risk of severe complications, particularly liver cancer, one of the leading causes of cancer-related deaths globally. This paper proposes using machine learning models such as DenseNet-121, VGG-16, and ViT to detect and classify liver lesions on 2008 CT scan images across arterial, delay, plain, and venous phases. The lesions are categorized into liver cysts, hemangiomas, and hepatocellular carcinoma, aiming to improve the efficiency of screening and early diagnosis. The results show that the ViT model achieved an accuracy of up to 0.99 with a short training time. Additionally, the paper highlights the major challenges of manual data labeling, which requires a significant amount of skilled labor, consumes time, and incurs high costs. Furthermore, the paper suggests the use of active learning to automate part of the labeling process, reducing labor requirements, saving time and costs, while ensuring consistency and data quality.
