Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/82207
PIRA download icon_1.1View/Download Full Text
Title: Cloud-based automated clinical decision support system for detection and diagnosis of lung cancer in chest CT
Authors: Masood, A
Yang, P
Sheng, B
Li, HT
Li, P 
Qin, J 
Lanfranchi, V
Kim, JM
Feng, DD
Issue Date: 2019
Source: IEEE journal of translational engineering in health and medicine-JTEHM, 4 Dec. 2019, v. 8, p. 1-13
Abstract: Lung cancer is a major cause for cancer-related deaths. The detection of pulmonary cancer in the early stages can highly increase survival rate. Manual delineation of lung nodules by radiologists is a tedious task. We developed a novel computer-aided decision support system for lung nodule detection based on a 3D Deep Convolutional Neural Network (3DDCNN) for assisting the radiologists. Our decision support system provides a second opinion to the radiologists in lung cancer diagnostic decision making. In order to leverage 3-dimensional information from Computed Tomography (CT) scans, we applied median intensity projection and multi-Region Proposal Network (mRPN) for automatic selection of potential region-of-interests. Our Computer Aided Diagnosis (CAD) system has been trained and validated using LUNA16, ANODE09, and LIDC-IDR datasets; the experiments demonstrate the superior performance of our system, attaining sensitivity, specificity, AUROC, accuracy, of 98.4, 92, 96 and 98.51 with 2.1 FPs per scan. We integrated cloud computing, trained and validated our Cloud-Based 3DDCNN on the datasets provided by Shanghai Sixth People's Hospital, as well as LUNA16, ANODE09, and LIDC-IDR. Our system outperformed the state-of-the-art systems and obtained an impressive 98.7 sensitivity at 1.97 FPs per scan. This shows the potentials of deep learning, in combination with cloud computing, for accurate and efficient lung nodule detection via CT imaging, which could help doctors and radiologists in treating lung cancer patients.
Keywords: Cancer
Lung
Computed tomography
Training
Solid modeling
Cloud computing
Machine learning
Computer-aided diagnosis
Nodule detection
Cloud computing
Computed tomography
Lung cancer
Publisher: Institute of Electrical and Electronics Engineers
Journal: IEEE journal of translational engineering in health and medicine-JTEHM 
EISSN: 2168-2372
DOI: 10.1109/JTEHM.2019.2955458
Rights: This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see http://creativecommons.org/licenses/by/4.0/
The following publication A. Masood et al., "Cloud-Based Automated Clinical Decision Support System for Detection and Diagnosis of Lung Cancer in Chest CT," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, pp. 1-13, 2020, Art no. 4300113, 1-13 is available at https://dx.doi.org/10.1109/JTEHM.2019.2955458
Appears in Collections:Journal/Magazine Article

Files in This Item:
File Description SizeFormat 
Masood_Cloud-Based_Automated_Clinical.pdf2.29 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page views

37
Citations as of May 15, 2022

Downloads

44
Citations as of May 15, 2022

SCOPUSTM   
Citations

17
Citations as of May 12, 2022

WEB OF SCIENCETM
Citations

10
Citations as of May 12, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.