Machine Learning in Early Cancer Detection: A Review of Methods and Applications

Cancer ranks among the diseases with the highest global mortality and complication rates. Early detection of cancer can significantly improve patients survival rates and burden of treatment costs. However, traditional methods such as Computed Tomography (CT) and Nuclear Magnetic Resonance Imaging (MRI) have a high false positive rate and limited accuracy when detecting cancer in the early stages. In the past few years, machine learning has appeared as a crucial tool for pattern recognition in biomedical data. By combining machine learning with traditional detection methods, new approaches and possibilities for early cancer detection have been explored. This review summarizes and compares the applications of machine learning in the early detection of cancer. The paper discusses the current status and challenges of machine learning in early cancer applications; analyzes the advantages and limitations of common machine learning technology techniques and methods in clinical practice translation; and explores future directions and possible addressing solutions.