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

Rong Fan*, Sei Ichiro Kamata, Yawen Chen

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationThirteenth International Conference on Digital Image Processing, ICDIP 2021
EditorsXudong Jiang, Hiroshi Fujita
PublisherSPIE
ISBN (Electronic)9781510646001
DOIs
Publication statusPublished - 2021
Event13th International Conference on Digital Image Processing, ICDIP 2021 - Singapore, Singapore
Duration: 2021 May 202021 May 23

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11878
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference13th International Conference on Digital Image Processing, ICDIP 2021
Country/TerritorySingapore
CitySingapore
Period21/5/2021/5/23

Keywords

  • Attention mechanism
  • Computer-aided system
  • ISODATA
  • Local density
  • Pulmonary nodule detection

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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