Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images

Anum Masood, Bin Sheng, Ping Li, Xuhong Hou, Xiaoer Wei, Jing Qin, Dagan Feng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

79 Citations (Scopus)

Abstract

Pulmonary cancer is considered as one of the major causes of death worldwide. For the detection of lung cancer, computer-assisted diagnosis (CADx) systems have been designed. Internet-of-Things (IoT) has enabled ubiquitous internet access to biomedical datasets and techniques; in result, the progress in CADx is significant. Unlike the conventional CADx, deep learning techniques have the basic advantage of an automatic exploitation feature as they have the ability to learn mid and high level image representations. We proposed a Computer-Assisted Decision Support System in Pulmonary Cancer by using the novel deep learning based model and metastasis information obtained from MBAN (Medical Body Area Network). The proposed model, DFCNet, is based on the deep fully convolutional neural network (FCNN) which is used for classification of each detected pulmonary nodule into four lung cancer stages. The performance of proposed work is evaluated on different datasets with varying scan conditions. Comparison of proposed classifier is done with the existing CNN techniques. Overall accuracy of CNN and DFCNet was 77.6% and 84.58%, respectively. Experimental results illustrate the effectiveness of proposed method for the detection and classification of lung cancer nodules. These results demonstrate the potential for the proposed technique in helping the radiologists in improving nodule detection accuracy with efficiency.

Original languageEnglish
Pages (from-to)117-128
Number of pages12
JournalJournal of Biomedical Informatics
Volume79
DOIs
Publication statusPublished - Mar 2018

Keywords

  • Convolutional neural networks (CNN)
  • Deep learning
  • Lung cancer stages
  • MBAN (Medical Body Area Network)
  • mIoT (medical Internet of Things)
  • Nodule detection

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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