Automated Decision Support System for Lung Cancer Detection and Classification via Enhanced RFCN with Multilayer Fusion RPN

Anum Masood, Bin Sheng, Po Yang, Ping Li, Huating Li, Jinman Kim, David Dagan Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

8 Citations (Scopus)


Detection of lung cancer at early stages is critical, in most of the cases radiologists read computed tomography (CT) images to prescribe follow-up treatment. The conventional method for detecting nodule presence in CT images is tedious. In this article, we propose an enhanced multidimensional region-based fully convolutional network (mRFCN) based automated decision support system for lung nodule detection and classification. The mRFCN is used as an image classifier backbone for feature extraction along with the novel multilayer fusion region proposal network (mLRPN) with position-sensitive score maps being explored. We applied a median intensity projection to leverage three-dimensional information from CT scans and introduced deconvolutional layer to adopt proposed mLRPN in our architecture to automatically select the potential region of interest. Our system has been trained and evaluated using LIDC dataset, and the experimental results showed promising detection performance in comparison to the state-of-the-art nodule detection/classification methods, achieving a sensitivity of 98.1% and classification accuracy of 97.91%.

Original languageEnglish
Article number9006938
Pages (from-to)7791-7801
Number of pages11
JournalIEEE Transactions on Industrial Informatics
Issue number12
Publication statusPublished - Dec 2020


  • Computer-aided systems
  • convolutional neural network (CNN)
  • lung cancer
  • nodule classification

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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