TY - JOUR
T1 - Automated decision support system for lung cancer detection and classification via enhanced RFCN with multilayer fusion RPN
AU - Masood, Anum
AU - Sheng, Bin
AU - Yang, Po
AU - Li, Ping
AU - Li, Huating
AU - Kim, Jinman
AU - Feng, David Dagan
N1 - Funding Information:
Manuscript received September 30, 2019; revised January 15, 2020; accepted February 1, 2020. Date of publication February 21, 2020; date of current version September 18, 2020. This work was supported in part by the National Natural Science Foundation of China under Grant 61872241 and Grant 61572316, in part by the National Key Research and Development Program of China under Grant 2017YFE0104000 and Grant 2016YFC1300302, and in part by the Science and Technology Commission of Shanghai Municipality under Grant 18410750700, Grant 17411952600, and Grant 16DZ0501100. Paper no. TII-19-4473. (Corresponding authors: Bin Sheng; Huating Li.) Anum Masood is with the Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China, and also with the Department of Computer Science, COM-SATS University Islamabad, Islamabad 45550, Pakistan (e-mail: [email protected]).
Publisher Copyright:
© 2005-2012 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/12
Y1 - 2020/12
N2 - Detection of lung cancer at early stages is critical, in most of the cases radiologists read computed tomography (CT) images to prescribe follow-up treatment. The conventional method for detecting nodule presence in CT images is tedious. In this article, we propose an enhanced multidimensional region-based fully convolutional network (mRFCN) based automated decision support system for lung nodule detection and classification. The mRFCN is used as an image classifier backbone for feature extraction along with the novel multilayer fusion region proposal network (mLRPN) with position-sensitive score maps being explored. We applied a median intensity projection to leverage three-dimensional information from CT scans and introduced deconvolutional layer to adopt proposed mLRPN in our architecture to automatically select the potential region of interest. Our system has been trained and evaluated using LIDC dataset, and the experimental results showed promising detection performance in comparison to the state-of-the-art nodule detection/classification methods, achieving a sensitivity of 98.1% and classification accuracy of 97.91%.
AB - Detection of lung cancer at early stages is critical, in most of the cases radiologists read computed tomography (CT) images to prescribe follow-up treatment. The conventional method for detecting nodule presence in CT images is tedious. In this article, we propose an enhanced multidimensional region-based fully convolutional network (mRFCN) based automated decision support system for lung nodule detection and classification. The mRFCN is used as an image classifier backbone for feature extraction along with the novel multilayer fusion region proposal network (mLRPN) with position-sensitive score maps being explored. We applied a median intensity projection to leverage three-dimensional information from CT scans and introduced deconvolutional layer to adopt proposed mLRPN in our architecture to automatically select the potential region of interest. Our system has been trained and evaluated using LIDC dataset, and the experimental results showed promising detection performance in comparison to the state-of-the-art nodule detection/classification methods, achieving a sensitivity of 98.1% and classification accuracy of 97.91%.
KW - Computer-aided systems
KW - convolutional neural network (CNN)
KW - lung cancer
KW - nodule classification
UR - http://www.scopus.com/inward/record.url?scp=85089437975&partnerID=8YFLogxK
U2 - 10.1109/TII.2020.2972918
DO - 10.1109/TII.2020.2972918
M3 - Journal article
AN - SCOPUS:85089437975
SN - 1551-3203
VL - 16
SP - 7791
EP - 7801
JO - IEEE Transactions on Industrial Informatics
JF - IEEE Transactions on Industrial Informatics
IS - 12
ER -
