Application of Machine Learning in Cell Detection

In recent years, machine learning algorithms have seen extensive application in chemical science, especially in cell detection technologies. Machine learning, a branch of artificial intelligence, is designed to automatically discover patterns in data. This review provides an overview of cell detection methods such as bright-field microscopy (BL), dark-field microscopy (DL), surface-enhanced Raman scattering (SERS), and fluorescence detection (FL). We highlight key computational models like support vector machines and convolutional neural networks that significantly enhance the precision and efficiency of automated cell detection. Relevant research applications are discussed, along with future prospects for machine learning in cell analysis.