Research on intelligent assistant diagnosis method of CT image for lung nodule based on mobile computing

Mobile computing techniques have facilitated our life greatly, but it is rarely used in medical computing. The automatic diagnosis of benign and malignant pulmonary nodules is of great significance for the further treatment of patients. Therefore, taking the three-dimensional nature of clinical pulmonary nodules into account, in a mobile computing environment, an algorithm that combines three-dimensional deep and visual features (CTDDV) is proposed to achieve the classification of benign and malignant pulmonary nodules. In the new framework, deep features, visual features as well as shape descriptors were extracted using different three-dimensional algorithms. Then, Multiple Kernel Adaboost (MKAdaboost) classifiers were trained for each type of feature, and the results of the three classifiers were combined to distinguish lung nodules. We compared the four most advanced nodule classification methods on the LIDC-IDRI dataset. The results showed that our proposed CTDDV algorithm has achieved higher classification performance.