  Breast cancer is one of the most serious types of cancer that can occur in
women. The automatic diagnosis of breast cancer by analyzing histological
images (HIs) is important for patients and their prognosis. The classification
of HIs provides clinicians with an accurate understanding of diseases and
allows them to treat patients more efficiently. Deep learning (DL) approaches
have been successfully employed in a variety of fields, particularly medical
imaging, due to their capacity to extract features automatically. This study
aims to classify different types of breast cancer using HIs. In this research,
we present an enhanced capsule network that extracts multi-scale features using
the Res2Net block and four additional convolutional layers. Furthermore, the
proposed method has fewer parameters due to using small convolutional kernels
and the Res2Net block. As a result, the new method outperforms the old ones
since it automatically learns the best possible features. The testing results
show that the model outperformed the previous DL methods.

Image and Video Processing;Computer Vision and Pattern Recognition