Figure 2:

Framework of the proposed model. N successive sections before and after the center section are collected together as the input. Convolution is performed on each image, and feature maps are extracted using the DenseNet model. The features are fed into a regional proposal network (RPN) to obtain potential regions first, then features inside the proposed regions are further processed to obtain both nodule classification and nodule location. ROI = region of interest.