Automated diagnosis and classification of liver cancers using deep learning techniques: a systematic review

Abstract

Abstract Liver cancer is one of the main causes of cancer-related mortality globally. It is a rising threat with over 700,000 deaths and 800,000 new cases annually. To tackle this, deep learning (DL) artificial intelligence (AI) tools have been researched extensively to create predictive models that aid in the early diagnosis and classification of liver cancers. The liver, however, is prone to metastasis from various gastrointestinal cancers, making it difficult to correctly classify tumors present in the organ. This paper is a thorough systematic evaluation of several published studies that used deep learning algorithms to diagnose, classify, and predict common liver tumors, including but not limited to Hepatocellular carcinoma and Intrahepatic cholangiocarcinoma. Articles selected in this review have been published between 2016 and 2024 from Google Scholar, Research Gate, Science Direct, IEEE Xplore, Springer Link, and other resources as per the PRISMA guidelines. Amongst these, the highest recorded accuracy was 99.38%, with other studies showing close results at 97% and below. The highest recorded sensitivity was 100%. Selected studies were also analyzed based on their choice of dataset, DL algorithm, and preprocessing techniques. Magnetic resonance imaging and computed tomography (CT) are by far the preferred medium for imaging data because of their high resolution; however, CT is used more often because it’s cheaper and provides a better view of extrahepatic space. Genetic datasets showed great potential in differentiating between primary and secondary liver cancers. Nonetheless, several gaps were identified across the current literature. The problem of accurately differentiating cancer types in the liver still persists. However, the use of 3D imaging datasets aids in this significantly. Though, it is clear that 3D datasets are underutilized. Dynamic imaging is also beneficial and should be considered more often. For future research, we strongly recommend the use of Gaussian Mixture Model, Watershed Transform, Sparsity classification, DLIR and Siamese algorithms as they each showed promising results. Lastly, we have shown a benchmark framework for a predictive AI model that can be referred to develop future models. Though numerous past methods have produced excellent prediction results, the prevalence of liver cancer-related deaths has not yet been reduced. To address the difficulties in the field of cancer prediction, more thorough research is required, along with the development of more efficient decision support systems using deep learning.

Keywords

• Liver cancer
• Artificial intelligence
• AI-based prediction
• Diagnosis
• Decision support system
• Deep learning