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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.
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Authors and Affiliations

Aleksandra Maria Osowska-Kurczab
1
ORCID: ORCID
Tomasz Markiewicz
1 2
ORCID: ORCID
Miroslaw Dziekiewicz
2
Malgorzata Lorent
2

  1. Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland
  2. Military Institute of Medicine, ul. Szaserów 128, 04-141 Warsaw, Poland

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