Glomerulosclerosis detection with pre-trained CNNs ensemble-Reference-Cited by-同舟云学术

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Glomerulosclerosis detection with pre-trained CNNs ensemble

Published:2022-12-08 Issue: Volume: Page:
ISSN:0943-4062
Container-title:Computational Statistics
language:en
Short-container-title:Comput Stat

Author:

Santos Justino^ORCID,Silva Romuere,Oliveira Luciano,Santos Washington,Aldeman Nayze,Duarte Angelo,Veras Rodrigo

Funder

Conselho Nacional de Desenvolvimento Científico e Tecnológico

Universidade Estadual de Feira de Santana

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://link.springer.com/content/pdf/10.1007/s00180-022-01307-3.pdf

Reference45 articles.

1. Alves E, Souza Filho JB, Kritski AL (2019) An ensemble approach for supporting the respiratory isolation of presumed tuberculosis inpatients. Neurocomputing 331:289–300

2. Araújo IC, Schnitman L, Duarte AA et al (2017) Automated detection of segmental glomerulosclerosis in kidney histopathology. In: XIII Brazilian Congress on Computational Intelligence, p 12

3. Barros GO, Navarro B, Duarte A et al (2017) Pathospotter-k: a computational tool for the automatic identification of glomerular lesions in histological images of kidneys. Sci Rep 7(1):1–8

4. Barros GO, Wanderley DC, Rebouças LO et al (2022) Podnet: ensemble-based classification of podocytopathy on kidney glomerular images. In: VISIGRAPP (5: VISAPP), pp 405–412

5. Bevilacqua V, Pietroleonardo N, Triggiani V et al (2017) An innovative neural network framework to classify blood vessels and tubules based on haralick features evaluated in histological images of kidney biopsy. Neurocomputing 228:143–153. https://doi.org/10.1016/j.neucom.2016.09.091

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