Open AccessComputer-aided classification of lung nodules on CT images with expert knowledge
Author(s) -
Chuangye Wan,
Ling Ma,
Xiabi Liu,
Baowei Fei
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2581888
Subject(s) - computer science , artificial intelligence , lung cancer , nodule (geology) , benchmark (surveying) , metric (unit) , pattern recognition (psychology) , radiology , medicine , pathology , paleontology , operations management , geodesy , economics , biology , geography
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.