Multi-feature ensemble system in the renal tumour classification task

• Autorzy: Osowska-Kurczab Aleksandra Maria , Markiewicz Tomasz , Dziekiewicz Miroslaw , Lorent Malgorzata

Identyfikatory

DOI

10.24425/bpasts.2021.136749

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

medical imaging; renal cell carcinoma; convolutional neural networks; textural features; support vector machine; computer vision; deep learning

PL

technika deep learning; obrazowanie medyczne; rak nerkowokomórkowy; konwolucyjne sieci neuronowe; cechy tekstury; maszyna wektorów nośnych; wizja komputerowa; głęboka nauka

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2021
• Tom: Vol. 69, nr 3
• Strony: art. no. e136749
• Opis fizyczny: Bibliogr. 34 poz., rys., tab.

Twórcy

autor

Osowska-Kurczab Aleksandra Maria

• Warsaw University of Technology, ul. Koszykowa 75, 00-662 Warsaw, Poland

• https://orcid.org/0000-0001-5764-522X

autor

Markiewicz Tomasz

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

autor

Dziekiewicz Miroslaw

• Military Institute of Medicine, ul. Szaserów 128, 04-141 Warsaw, Poland

autor

Lorent Malgorzata

• Military Institute of Medicine, ul. Szaserów 128, 04-141 Warsaw, Poland

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).