@ARTICLE{Osowska-Kurczab_Aleksandra_Maria_Multi-feature_2021,
 author={Osowska-Kurczab, Aleksandra Maria and Markiewicz, Tomasz and Dziekiewicz, Miroslaw and Lorent, Malgorzata},
 volume={69},
 number={3},
 journal={Bulletin of the Polish Academy of Sciences Technical Sciences},
 pages={e136749},
 howpublished={online},
 year={2021},
 abstract={Recently, the 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 a 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.},
 type={Article},
 title={Multi-feature ensemble system in the renal tumour classification task},
 URL={http://www.czasopisma.pan.pl/Content/119433/PDF/02_01972_Bpast.No.69(3)_23.06.21_Druk.pdf},
 doi={10.24425/bpasts.2021.136749},
 keywords={medical imaging, renal cell carcinoma, convolutional neural networks, textural features, support vector machine, computer vision, deep learning},
}