Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/105627
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Computing | en_US |
dc.creator | Masood, A | en_US |
dc.creator | Sheng, B | en_US |
dc.creator | Li, P | en_US |
dc.creator | Hou, X | en_US |
dc.creator | Wei, X | en_US |
dc.creator | Qin, J | en_US |
dc.creator | Feng, D | en_US |
dc.date.accessioned | 2024-04-15T07:35:30Z | - |
dc.date.available | 2024-04-15T07:35:30Z | - |
dc.identifier.issn | 1532-0464 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/105627 | - |
dc.language.iso | en | en_US |
dc.publisher | Academic Press | en_US |
dc.rights | © 2018 Elsevier Inc. All rights reserved. | en_US |
dc.rights | ©2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | en_US |
dc.rights | The following publication Masood, A., Sheng, B., Li, P., Hou, X., Wei, X., Qin, J., & Feng, D. (2018). Computer-assisted decision support system in pulmonary cancer detection and stage classification on CT images. Journal of biomedical informatics, 79, 117-128 is available at https://doi.org/10.1016/j.jbi.2018.01.005. | en_US |
dc.subject | Convolutional neural networks (CNN) | en_US |
dc.subject | Deep learning | en_US |
dc.subject | Lung cancer stages | en_US |
dc.subject | MBAN (Medical Body Area Network) | en_US |
dc.subject | mIoT (medical Internet of Things) | en_US |
dc.subject | Nodule detection | en_US |
dc.title | Computer-assisted decision Support system in pulmonary cancer detection and stage classification on CT images | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 117 | en_US |
dc.identifier.epage | 128 | en_US |
dc.identifier.volume | 79 | en_US |
dc.identifier.doi | 10.1016/j.jbi.2018.01.005 | en_US |
dcterms.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. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Journal of biomedical informatics, Mar. 2018, v. 79, p. 117-128 | en_US |
dcterms.isPartOf | Journal of biomedical informatics | en_US |
dcterms.issued | 2018-03 | - |
dc.identifier.scopus | 2-s2.0-85040686779 | - |
dc.identifier.pmid | 29366586 | - |
dc.identifier.eissn | 1532-0480 | en_US |
dc.description.validate | 202402 bcch | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | COMP-0965 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.fundingSource | Others | en_US |
dc.description.fundingText | National Natural Science Foundation of China; National High-tech R&D Program of China (863 Program); Key Program for International S&T Cooperation Project of China; Science and Technology Commission of Shanghai Municipality; Shanghai Jiao Tong University; The Hong Kong Polytechnic University | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 21849473 | - |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Li_Computer-Assisted_Decision_Support.pdf | Pre-Published version | 1.74 MB | Adobe PDF | View/Open |
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