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The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Effect of the Pixel Interpolation Method for Downsampling Medical Images on Deep Learning Accuracy

Hirahara, Daisuke, Takaya, Eichi, Kadowaki, Mizuki, Kobayashi, Yasuyuki, Ueda, Takuya·2021·Journal of Computer and Communications
DOI 10.4236/jcc.2021.911010
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Figure 1 CNN structure (Fh: input height, Fw: input width, Oh: output height, Ow: output width, P: padding, S: stride; kernel size: 5, stride: 1, padding: 0, dropout: 0.5).
Figure 1 CNN structure (Fh: input height, Fw: input width, Oh: output height, Ow: output width, P: padding, S: stride; kernel size: 5, stride: 1, padding: 0, dropout: 0.5).CC-BY · From the PDF
Figure 2 Maximum classification accuracy of the CNN according to the interpolation method and image size. (NN: Nearest neighbor, BL: Bilinear, HM: Hamming window, BC: Bicubic, LC: Lanczos).
Figure 2 Maximum classification accuracy of the CNN according to the interpolation method and image size. (NN: Nearest neighbor, BL: Bilinear, HM: Hamming window, BC: Bicubic, LC: Lanczos).CC-BY · From the PDF

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Research works printed but not linked: 15

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  15. Tan, M. and Quoc, V.L. (2019) EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. arXiv preprint arXiv:1905.11946. · arXiv

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Metadata

Authors
Hirahara, D., Takaya, E., Kadowaki, M., Kobayashi, Y., & Ueda, T.
Venue
Journal of Computer and Communications
Publisher
Scientific Research Publishing, Inc.
Published
2021
Volume
09
Issue
11
Pages
150-156
ISSN
  • 2327-5219
  • 2327-5227
DOI
10.4236/jcc.2021.911010
Cited by
24
References
1
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)

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