Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/77426
Title: Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images
Authors: Masood, A
Sheng, B
Li, P
Hou, X
Wei, X
Qin, J 
Feng, D
Keywords: Convolutional neural networks (CNN)
Deep learning
Lung cancer stages
MBAN (Medical Body Area Network)
MIoT (medical Internet of Things)
Nodule detection
Issue Date: 2018
Publisher: Academic Press
Source: Journal of biomedical informatics, 2018, v. 79, p. 117-128 How to cite?
Journal: Journal of biomedical informatics 
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.
URI: http://hdl.handle.net/10397/77426
ISSN: 1532-0464
EISSN: 1532-0480
DOI: 10.1016/j.jbi.2018.01.005
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