Paper Info
Title
Automated Decision Support System for Lung Cancer Detection and Classification via Enhanced RFCN With Multilayer Fusion RPN
Abstract
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%.
Year
DOI
Venue
2020
10.1109/TII.2020.2972918
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Cancer,Lung,Computed tomography,Feature extraction,Training,Proposals,Informatics
Journal
16
Issue
ISSN
Citations 
12
1551-3203
2
PageRank 
References 
Authors
0.46
0
7
Name
Order
Citations
PageRank
Anum Masood120.46
Bin Sheng236861.19
Po Yang36412.75
Ping Li420240.76
Huating Li5225.14
Jinman Kim650465.66
David Dagan Feng73329413.76