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.
@article{arxiv.2211.00749,
title = {ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification},
author = {Amira Alotaibi and Tarik Alafif and Faris Alkhilaiwi and Yasser Alatawi and Hassan Althobaiti and Abdulmajeed Alrefaei and Yousef M Hawsawi and Tin Nguyen},
journal= {arXiv preprint arXiv:2211.00749},
year = {2022}
}