Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques
Article
Atmakuru, Anirudh, Chakraborty, Subrata, Faust, Oliver, Salvi, Massimo, Barua, Prabal Datta, Molinari, Filippo, Acharya, U.R. and Homaira, Nusrat. 2024. "Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques." Expert Systems with Applications. 255 (Part B). https://doi.org/10.1016/j.eswa.2024.124665
Article Title | Deep learning in radiology for lung cancer diagnostics: A systematic review of classification, segmentation, and predictive modeling techniques |
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ERA Journal ID | 17852 |
Article Category | Article |
Authors | Atmakuru, Anirudh, Chakraborty, Subrata, Faust, Oliver, Salvi, Massimo, Barua, Prabal Datta, Molinari, Filippo, Acharya, U.R. and Homaira, Nusrat |
Journal Title | Expert Systems with Applications |
Journal Citation | 255 (Part B) |
Article Number | 124665 |
Number of Pages | 23 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0957-4174 |
1873-6793 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2024.124665 |
Web Address (URL) | https://www.sciencedirect.com/science/article/abs/pii/S095741742401532X |
Abstract | This study presents a comprehensive systematic review focusing on the applications of deep learning techniques in lung cancer radiomics. Through a rigorous screening process of 589 scientific publications following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we selected 153 papers for an in-depth analysis. These papers were categorized based on imaging modality, deep learning model type, and practical applications in lung cancer, such as detection and survival prediction. We specifically emphasized deep learning models and examined their strengths and limitations for each application and imaging modality. Furthermore, we identified potential limitations within the field and proposed future research directions. |
Keywords | Classification |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 420311. Health systems |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | University of Massachusetts, United States |
University of New England | |
University of Technology Sydney | |
Anglia Ruskin University, United Kingdom | |
Polytechnic University of Turin, Italy | |
School of Business | |
School of Mathematics, Physics and Computing | |
Cogninet Australia, Australia | |
Australian International Institute of Higher Education, Australia | |
Taylor’s University, Malaysia | |
SRM Institute of Science and Technology, India | |
Kumamoto University, Japan | |
University of Sydney | |
University of New South Wales | |
BRAC University, Bangladesh |
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https://research.usq.edu.au/item/z998x/deep-learning-in-radiology-for-lung-cancer-diagnostics-a-systematic-review-of-classification-segmentation-and-predictive-modeling-techniques
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