HER2 binary classification performance for the (a) EfficientNetV2B0 subnetworks, (b) EfficientNetV2B0 subnetworks pretrained on ImageNet, (c) ATHER2, and (d) ATHER2 pretrained on BreakHis (BCI dataset – IHC images).

HER2 binary classification performance for the (a) EfficientNetV2B0 subnetworks, (b) EfficientNetV2B0 subnetworks pretrained on ImageNet, (c) ATHER2, and (d) ATHER2 pretrained on BreakHis (BCI dataset – IHC images).

<p>HER2 binary classification performance for the (a) EfficientNetV2B0 subnetworks, (b) EfficientNetV2B0 subnetworks pretrained on ImageNet, (c) ATHER2, and (d) ATHER2 pretrained on BreakHis (BCI dataset – IHC images).</p>

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Bibliographic Details
Main Author: Lamiaa Abdel-Hamid (22250618) (author)
Other Authors: Pratheepan Yogarajah (22250621) (author), Safy Hosny Ahmed Tealab (22250624) (author)
Published: 2025
Subjects:
Medicine
Cell Biology
Space Science
Biological Sciences not elsewhere classified
required laboratory equipment
final testing phases
complex classification tasks
based her2 scoring
aggressive cancer type
requires special diagnosis
two lightweight models
customized lightweight network
multiclass classifications regardless
e stained images
lightweight models
multiclass classifications
time diagnosis
network pruning
network parameters
binary classifications
treatment methods
transfer learning
rural regions
routinely performed
readily available
patient samples
networks resulted
mobile phones
generalization capability
embedded systems
efficientnetv2b0 pruned
efficiently trained
effectively evaluate
e images
different datasets
developing countries
deep networks
deep learning
constrained devices
cbam ).
attention mechanisms
accuracies ranging
80 %.
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Summary:<p>HER2 binary classification performance for the (a) EfficientNetV2B0 subnetworks, (b) EfficientNetV2B0 subnetworks pretrained on ImageNet, (c) ATHER2, and (d) ATHER2 pretrained on BreakHis (BCI dataset – IHC images).</p>

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