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Journal of biomedical informatics v.79, 2018년, pp.117 - 128   SCI SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

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

Masood, Anum (Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, China ) ; Sheng, Bin (Dept. of Computer Science and Engineering, Shanghai Jiao Tong University, China ) ; Li, Ping (Faculty of Information Technology, Macau University of Science and Technology, Macau ) ; Hou, Xuhong (Shanghai Jiao Tong University Affiliated Sixth People's Hospital, China ) ; Wei, Xiaoer (Shanghai Jiao Tong University Affiliated Sixth People's Hospital, China ) ; Qin, Jing (School of Nursing, The Hong Kong Polytechnic University, Hong Kong ) ; Feng, Dagan (School of Information Technologies, The University of Sydney, Australia ) ;
  • 초록  

    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. Highlights A computer-aided decision support system based on FCNN is proposed. ROI-based segmentation has improved the nodule detection through DFCNet. Cancer stage physiological symptoms prevalence and DFCNet output are used for cancer stage classification. Graphical abstract [DISPLAY OMISSION]


  • 주제어

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

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