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Wyświetlanie 1-3 z 3
Tytuł:
Metrological analysis of additively manufactured copies of a fossil skull
Autorzy:
Rucki, Mirosław
Garashchenko, Yaroslav
Kogan, Ilja
Ryba, Tomasz
Powiązania:
https://bibliotekanauki.pl/articles/2173724.pdf
Data publikacji:
2022
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
additive manufacturing
Madygenerpeton
optical measurement
statistical analysis
produkcja dodatkowa
pomiar optyczny
analiza statystyczna
Opis:
This paper presents the results of a metrological analysis of the additively manufactured (AM) copies of a complex geometrical object, namely the fossil skull of Madygenerpeton pustulatum. This fossil represents the unique remains of an extinct “reptiliomorph amphibian” of high importance for palaeontological science. For this research, the surface was scanned and twelve different copies were 3D-printed using various devices, materials, and AM techniques. The same digitized model was used as a reference to compare with the surfaces obtained by Mitutoyo Coordinate Measuring Machine (CMM) CRYSTA-Apex S 9166 for each copy. The fidelity of the copies was assessed through statistical analysis of the distances between compared surfaces. The methodology provided a good background for the choice of the most accurate copies and the elimination of the less accurate ones. The proposed approach can be applied to any object of complex geometry when reproduction accuracy is to be assessed.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2022, 70, 6; art. no. e143827
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-feature ensemble system in the renal tumour classification task
Autorzy:
Osowska-Kurczab, Aleksandra Maria
Markiewicz, Tomasz
Dziekiewicz, Miroslaw
Lorent, Malgorzata
Powiązania:
https://bibliotekanauki.pl/articles/2173572.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
medical imaging
renal cell carcinoma
convolutional neural networks
textural features
support vector machine
computer vision
deep learning
technika deep learning
obrazowanie medyczne
rak nerkowokomórkowy
konwolucyjne sieci neuronowe
cechy tekstury
maszyna wektorów nośnych
wizja komputerowa
głęboka nauka
Opis:
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.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; art. no. e136749
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
Multi-feature ensemble system in the renal tumour classification task
Autorzy:
Osowska-Kurczab, Aleksandra Maria
Markiewicz, Tomasz
Dziekiewicz, Miroslaw
Lorent, Malgorzata
Powiązania:
https://bibliotekanauki.pl/articles/2128157.pdf
Data publikacji:
2021
Wydawca:
Polska Akademia Nauk. Czytelnia Czasopism PAN
Tematy:
medical imaging
renal cell carcinoma
convolutional neural networks
textural features
support vector machine
computer vision
deep learning
technika deep learning
obrazowanie medyczne
rak nerkowokomórkowy
konwolucyjne sieci neuronowe
cechy tekstury
maszyna wektorów nośnych
wizja komputerowa
głęboka nauka
Opis:
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.
Źródło:
Bulletin of the Polish Academy of Sciences. Technical Sciences; 2021, 69, 3; e136749, 1--8
0239-7528
Pojawia się w:
Bulletin of the Polish Academy of Sciences. Technical Sciences
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-3 z 3

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