Multi-view convolutional recurrent neural networks for lung cancer nodule identification
Article
Naeem Abid, M., Zia, T., Ghafoor, M. and Windridge, D. 2021. Multi-view convolutional recurrent neural networks for lung cancer nodule identification. Neurocomputing. 453, pp. 299-311. https://doi.org/10.1016/j.neucom.2020.06.144
Type | Article |
---|---|
Title | Multi-view convolutional recurrent neural networks for lung cancer nodule identification |
Authors | Naeem Abid, M., Zia, T., Ghafoor, M. and Windridge, D. |
Abstract | Screening via low-dose Computer Tomography (CT) has been shown to reduce lung cancer mortality rates by at least 20%. However, the assessment of large numbers of CT scans by radiologists is cost intensive, and potentially produces varying and inconsistent results for differing radiologists (and also for temporally-separated assessments by the same radiologist). To overcome these challenges, computer aided diagnosis systems based on deep learning methods have proved an effective in automatic detection and classification of lung cancer. |
Publisher | Elsevier |
Journal | Neurocomputing |
ISSN | 0925-2312 |
Electronic | 1872-8286 |
Publication dates | |
Online | 23 Jan 2021 |
17 Sep 2021 | |
Publication process dates | |
Deposited | 11 Feb 2021 |
Accepted | 01 Jun 2020 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | © 2021. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/ |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.neucom.2020.06.144 |
Language | English |
https://repository.mdx.ac.uk/item/89438
Download files
59
total views42
total downloads0
views this month4
downloads this month