Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass
  Breast Cancer Classification
Kanan Mahammadli Abdullah Burkan Bereketoglu Ayse Gul Kabakci
Breast Cancer is the most common cancer among women, which is also visible in
men, and accounts for more than 1 in 10 new cancer diagnoses each year. It is
also the second most common cause of women who die from cancer. Hence, it
necessitates early detection and tailored treatment. Early detection can
provide appropriate and patient-based therapeutic schedules. Moreover, early
detection can also provide the type of cyst. This paper employs class-level
data augmentation, addressing the undersampled classes and raising their
detection rate. This approach suggests two key components: class-level data
augmentation on structure-preserving stain normalization techniques to
hematoxylin and eosin-stained images and transformer-based ViTNet architecture
via transfer learning for multiclass classification of breast cancer images.
This merger enables categorizing breast cancer images with advanced image
processing and deep learning as either benign or as one of four distinct
malignant subtypes by focusing on class-level augmentation and catering to
unique characteristics of each class with increasing precision of
classification on undersampled classes, which leads to lower mortality rates
associated with breast cancer. The paper aims to ease the duties of the medical
specialist by operating multiclass classification and categorizing the image
into benign or one of four different malignant types of breast cancers.