Multi-feature ensemble system in the renal tumour classification task
Authors:
- Aleksandra Osowska-Kurczab,
- Tomasz Markiewicz,
- Mirosław Dziekiewicz,
- Małgorzata Lorent
Abstract
Recently, analysis of medical imaging is gaining substantial research interest, due to advancements in the computer vision field. Automation of medical image analysis can significantly improve the diagnosis process and lead to better prioritization of patients waiting for medical consultation. This research is dedicated to building a multi-feature ensemble model which associates two independent methods of image description: textural features and deep learning. Different algorithms of classification were applied to single-phase computed tomography images containing 8 subtypes of renal neoplastic lesions. The final ensemble includes a textural description combined with support vector machine and various configurations of Convolutional Neural Networks. Results of experimental tests have proved that such a model can achieve 93.6% of weighted F1-score (tested in 10-fold cross validation mode). Improvement of performance of the best individual predictor totalled 3.5 percentage points.
- Record ID
- WUTbf5c7663db94439db0e975a49476273e
- Author
- Journal series
- Bulletin of the Polish Academy of Sciences, Technical Sciences, ISSN 0239-7528, e-ISSN 2300-1917
- Issue year
- 2021
- Pages
- 1-7
- Publication size in sheets
- 0.50
- Keywords in English
- medical imaging renal cell carcinoma convolutional neural networks textural features support vector machine computer vision deep learning
- ASJC Classification
- ; ; ; ;
- DOI
- DOI:10.24425/bpasts.2021.136749 Opening in a new tab
- URL
- https://journals.pan.pl/bpasts/136749 Opening in a new tab
- Language
- (en) English
- Score (nominal)
- 100
- Score source
- journalList
- Score
- = 100.0, 28-05-2022, ArticleFromJournal
- Publication indicators
- = 1; : 2018 = 1.293; : 2020 (2 years) = 1.662 - 2020 (5 years) =1.699
- Uniform Resource Identifier
- https://repo.pw.edu.pl/info/article/WUTbf5c7663db94439db0e975a49476273e/
- URN
urn:pw-repo:WUTbf5c7663db94439db0e975a49476273e
* presented citation count is obtained through Internet information analysis and it is close to the number calculated by the Publish or PerishOpening in a new tab system.