ViT-DeiT: An Ensemble Model for Breast Cancer Histopathological Images Classification
This work addresses the need for accurate and timely diagnosis of breast cancer in women, but it is incremental as it combines existing transformer models without introducing new methodological innovations.
The authors tackled breast cancer classification from histopathological images using an ensemble of pre-trained Vision Transformer and Data-Efficient Image Transformer models, achieving 98.17% accuracy and over 98% in precision, recall, and F1 score on a public dataset.
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.