Discrimination of benign and malignant pulmonary tumors in computed tomography: effective priori information of fast learning network architecture

This study explores the influence of prior information for deep learning networks to discriminate the benign and malignant of pulmonary tumors in computed tomography. In this study, because the number of nodule samples is sparse, this study proposes the concept of Multiple-Window to provide prior knowledge for Convolutional neural network (CNN). In the Multiple-Window CNN, we use the 5 windows including lung window, abdomen window, bone window, and chest window to generate the nodule sample. The sparse number of nodule samples, through the characteristics of the CT image dynamic range, make more prior information in a limited amount of data. The results show that the increase of suitably prior information (window channel) be included, CNN performance has improved. When the input is original dicom image, the accuracy of CNN is 0.82, sensitivity is 0.82, and specificity is 0.82. When the input is 4 kinds channel of window type, the accuracy is 0.9, sensitivity is 0.84, and specificity is 0.96.