Table Info

Table 3.

Properties of pretrained networks

Pretrained CNN model	Layers	Model size (MB)	Parameters	Input size (pixels)
ResNet-18	18	44	11.7 × 10⁶	224 × 224
SqueezeNet	18	5.2	1.24 × 10⁶	227 × 227
DenseNet-201	201	77	20 × 10⁶	224 × 224
AlexNet	8	227	61 × 10⁶	227 × 227
Xception	71	85	22.9 × 10⁶	299 × 299
NASNet-Large	1244^#	332	88.9 × 10⁶	331 × 331

indicates the network does not consist of a linear sequence of modules; NASNet: neural search architecture network; ResNet: residual network; DenseNet: dense convolutional network

Declarations

Author contributions

TDP and XFS: Conceptualization. TDP: Methodology. TDP: Software. TDP, VR, BL, CF, and XFS: Formal analysis. BL and CF: Data curation. TDP: Writing—original draft. TDP, VR, BL, CF, and XFS: Writing—review & editing.

Conflicts of interest

The authors declare that there are no conflicts of interest.

Ethical approval

The ethical applications were approved by the Institutional Ethics Committee of Linköping University (Dnr 2012-107-31 and Dnr 2014-79-31).

Consent to participate

The informed consent to participate in the study was obtained from all participants.

Consent to publication

Not applicable.

Availability of data and materials

The IHC image data used in this study are available at the first author’s homepage: https://sites.google.com/view/tuan-d-pham/codes under the name “Rectal cancer biopsy”.

Funding

Not applicable.

Copyright

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