  Breast cancer is the most common cancer in the world and the second most
common type of cancer that causes death in women. The timely and accurate
diagnosis of breast cancer using histopathological images is crucial for
patient care and treatment. Pathologists can make more accurate diagnoses with
the help of a novel approach based on image processing. This approach is an
ensemble model of two types of pre-trained vision transformer models, namely,
Vision Transformer and Data-Efficient Image Transformer. The proposed ensemble
model classifies breast cancer histopathology images into eight classes, four
of which are categorized as benign, whereas the others are categorized as
malignant. A public dataset was used to evaluate the proposed model. The
experimental results showed 98.17% accuracy, 98.18% precision, 98.08% recall,
and a 98.12% F1 score.
