Computer-aided Classification of Lung Nodules on CT Images with Expert Knowledge

Accurate classification of pulmonary nodules in the CT images is critical for early detection of lung cancer as well as the assessment of the effect from COVID-19. In this paper, we propose a computer-aided classification method for lung nodules using expert knowledge. We use a decoupling metric learning model to describe the deep characteristics of the nodules and then calculate the similarity between the current nodule and the nodules in the database. By analyzing the returned nodules with the diagnosis information, we obtain the expert knowledge of similar nodules, based on which we make the decision of the current nodule. The proposed method has been evaluated on the benchmark LIDC-IDRI dataset and achieved an accuracy of 95.7% and AUC of 0.9901. The proposed classification method can have a variety of applications in lung cancer detection, diagnosis and therapy.

Automated summary

The study proposes a computer-aided method for accurate classification of lung nodules using expert knowledge, achieving high accuracy and AUC values on benchmark dataset. The method has great potential for various applications in lung cancer detection, diagnosis, and therapy.