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Breast Cancer is the most common cancer among women, which is also visible inmen, and accounts for more than 1 in 10 new cancer diagnoses each year. It isalso the second most common cause of women who die from cancer. Hence, itnecessitates early detection and tailored treatment. Early detection canprovide appropriate and patient-based therapeutic schedules. Moreover, earlydetection can also provide the type of cyst. This paper employs class-leveldata augmentation, addressing the undersampled classes and raising theirdetection rate. This approach suggests two key components: class-level dataaugmentation on structure-preserving stain normalization techniques tohematoxylin and eosin-stained images and transformer-based ViTNet architecturevia transfer learning for multiclass classification of breast cancer images.This merger enables categorizing breast cancer images with advanced imageprocessing and deep learning as either benign or as one of four distinctmalignant subtypes by focusing on class-level augmentation and catering tounique characteristics of each class with increasing precision ofclassification on undersampled classes, which leads to lower mortality ratesassociated with breast cancer. The paper aims to ease the duties of the medicalspecialist by operating multiclass classification and categorizing the imageinto benign or one of four different malignant types of breast cancers.

Class-Specific Data Augmentation: Bridging the Imbalance in Multiclass Breast Cancer Classification