Journal
JOURNAL OF BIOMEDICAL INFORMATICS
Volume 79, Issue -, Pages 117-128Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jbi.2018.01.005
Keywords
Lung cancer stages; Nodule detection; Deep learning; Convolutional neural networks (CNN); mIoT (medical Internet of Things); MBAN (Medical Body Area Network)
Funding
- National Natural Science Foundation of China [61572316, 61671290]
- National High-tech R&D Program of China (863 Program) [2015AA015904]
- Key Program for International S&T Cooperation Project of China [2016YFE0129500]
- Science and Technology Commission of Shanghai Municipality [16DZ0501100, 17411952600]
- General Program of Cross of Medicine and Engineering of SJTU [YG2015MS19]
- Shanghai Jiao Tong University [14JCY10]
- Research Grants Council of Hong Kong [28200215]
- Hong Kong Polytechnic University [1-ZE8J]
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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 interne 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.
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