Pulmonary nodule detection using improved faster R-CNN and 3D Resnet

Pulmonary nodule detection system consists of two steps: candidate detection and false positive reduction. To dynamically adapt the sizes and ratios of the nodules, Local Density based Iterative Self-Organizing Data Analysis Techniques Algorithm (D-ISODATA) is proposed for automated anchor boxes configuration. For candidate detection, instead of fixed anchor, D-ISODATA is utilized for automatically generate anchors to adapt to high variability of nodules. D-ISODATA initializes clustering center and removes noises based on the principle of maximum local density and further clustering is carried out with self-adaptability. In addition, attention mechanism is adopted in feature channels to enable the model to focus on nodule-related features. For false positive reduction, 3D Resnet is utilized to extract the three-dimensional features of nodules. Experiments are carried out on LUNA16 dataset and show out a sensitivity of 93.6% with 0.15 false positive per scan. The results show preferable performance of the proposed method.