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

Rong Fan; Sei-ichiro Kamata; Yawen Chen

doi:10.1117/12.2599884

30 June 2021 Pulmonary nodule detection using improved faster R-CNN and 3D Resnet

Rong Fan, Sei-ichiro Kamata, Yawen Chen

Proceedings Volume 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021); 118780F (2021) https://doi.org/10.1117/12.2599884
Event: Thirteenth International Conference on Digital Image Processing, 2021, Singapore, Singapore

Abstract

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.

Citation Download Citation

Rong Fan, Sei-ichiro Kamata, and Yawen Chen "Pulmonary nodule detection using improved faster R-CNN and 3D Resnet", Proc. SPIE 11878, Thirteenth International Conference on Digital Image Processing (ICDIP 2021), 118780F (30 June 2021); https://doi.org/10.1117/12.2599884

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