Multi-objective Lung Image Detection Method Based on Self-regulating Sparse Representation

There are more and more lung diseases, and the detection of lung images is an important research topic. The intensity and difficulty of manual detection are large, and sometimes there is a misjudgment. Based on the research of sparse expression theory, this paper improves the existing sparse expression algorithm, applies it to target tracking to realize multi-target tracking algorithm based on self-regulating sparse expression. A multi-objective lung image detection method based on self-regulating sparse expression is proposed for the judgment of nodules and tumors. Experiments show that the recognition rate of nodules in lung images can reach more than 95.5%, which improves the accuracy and real-time of target tracking, and will improve the means and level of assistant diagnosis and treatment in medicine.