Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection

Xuechen Li, Linlin Shen, Xinpeng Xie, Shiyun Huang, Zhien Xie, Xian Hong, Juan Yu

Research output: Journal PublicationArticlepeer-review

85 Citations (Scopus)


Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment for patients. Due to the limited number of radiologist and the huge number of chest x-ray radiographs (CXR) available for observation, a computer-aided detection scheme should be developed to assist radiologists in decision-making. While deep learning showed state-of-the-art performance in several computer vision applications, it has not been used for lung nodule detection on CXR. In this paper, a deep learning-based lung nodule detection method was proposed. We employed patch-based multi-resolution convolutional networks to extract the features and employed four different fusion methods for classification. The proposed method shows much better performance and is much more robust than those previously reported researches. For publicly available Japanese Society of Radiological Technology (JSRT) database, more than 99% of lung nodules can be detected when the false positives per image (FPs/image) was 0.2. The FAUC and R-CPM of the proposed method were 0.982 and 0.987, respectively. The proposed approach has the potential of applications in clinical practice.

Original languageEnglish
Article number101744
JournalArtificial Intelligence in Medicine
Publication statusPublished - Mar 2020
Externally publishedYes


  • Computer-aided detection
  • Lung nodule detection
  • Multi-resolution patch-based convolutional neural network
  • x-ray radiograph

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence


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