Optimisation analysis of pulmonary nodule diagnostic test based on deep belief nets

Abstract

At present, the rate of missed diagnosis of lung cancer is high. The reason is that the pulmonary nodule phenomenon cannot be effectively monitored due to various interference factors in the actual detection process. In order to improve the detection accuracy, this study combined with the actual situation to analyse the diversity of nodular shape and constructed a deep belief network-based diagnosis model for pulmonary nodules. At the same time, in order to improve the detection effect, this study sets the model to have multi-layer non-linear structure and analyses the previous clinical data to improve the model learning rate and training effect. In addition, in order to verify the performance of the model, the diagnostic effect of the model is studied by comparative experiments. The research shows that the model proposed in this study is higher than the traditional algorithm in detection accuracy, which can provide theoretical reference for subsequent related research.