  Diagnosis of breast cancer malignancy at the early stages is a crucial step
for controlling its side effects. Histopathological analysis provides a unique
opportunity for malignant breast cancer detection. However, such a task would
be tedious and time-consuming for the histopathologists. Deep Neural Networks
enable us to learn informative features directly from raw histopathological
images without manual feature extraction. Although Convolutional Neural
Networks (CNNs) have been the dominant architectures in the computer vision
realm, Transformer-based architectures have shown promising results in
different computer vision tasks. Although harnessing the capability of
Transformer-based architectures for medical image analysis seems interesting,
these architectures are large, have a significant number of trainable
parameters, and require large datasets to be trained on, which are usually rare
in the medical domain. It has been claimed and empirically proved that at least
part of the superior performance of Transformer-based architectures in Computer
Vision domain originates from patch embedding operation. In this paper, we
borrowed the previously introduced idea of integrating a fully Convolutional
Neural Network architecture with Patch Embedding operation and presented an
efficient CNN architecture for breast cancer malignancy detection from
histopathological images. Despite the number of parameters that is
significantly smaller than other methods, the accuracy performance metrics
achieved 97.65%, 98.92%, 99.21%, and 98.01% for 40x, 100x, 200x, and 400x
magnifications respectively. We took a step forward and modified the
architecture using Group Convolution and Channel Shuffling ideas and reduced
the number of trainable parameters even more with a negligible decline in
performance and achieved 95.42%, 98.16%, 96.05%, and 97.92% accuracy for the
mentioned magnifications respectively.
