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.

乳腺癌是妇女中最常见的癌症，也可见于男性，并在每年新诊断的癌症中占比超过十分之一，是妇女癌症死因的第二大常见原因。本研究采用了类别层级数据增强的方法，通过改善少数类别的检测率，使用结构保持染色归一化技术对血红蛋白和嗜酸性染剂染色的图像进行类别层级数据增强，并基于转移学习的 ViTNet 模型进行深度学习的多类别分类，以将乳腺癌图像分为良性和四种不同的恶性亚型，从而提高对乳腺癌患者的分类精度和降低乳腺癌相关的死亡率。