Magnification Independent Approach to Diagnose the Breast Cancer from Histopathological Images

Abstract

Breast cancer is one of the top cancers that cause death globally. Hematoxylin and Eosin-stained images diagnose biopsy tissue, and experts are usually upset with the closing opinion. Computer-assisted diagnosis techniques enable to cut costs and improve efficiency in this practice. Automation of breast cancer multi- class classification from microscopic images has a significant impact on computer-assisted breast cancer identification or prediction. The purpose of breast cancer multiclass classification is to classify different subtypes of breast cancer (Papillary, Adenosis, Mucinous, etc.) after identifying the Benign or Malignant Class. Yet, multi-class classification of breast cancer from microscopic imageries aspects dual major encounters: First, the excessive problems in methods of breast cancer multi-class classification compared to binary class classification (benign vs. malignant), and second, the slight variances in several classes because of the high-quality image inconsistency forms, high cohesiveness of malignant cells, and inconsistency of color distribution. As a result, although the automatic multi-class classification of breast cancer from microscopic images has considerable clinical importance, it has never been investigated. As we are dealing with microscopic imaging, magnification plays a vital role while classifying cancer. Existing literature techniques exclusively emphasize magnification-dependent binary or multi- class classification and do not continue the effort for magnification-independent breast cancer diagnosis. Using a proposed model inspired by Vision Transformer (ViT) model with multiple variations, this work offers a robust and novel breast cancer multi-class classification approach combined with a binary classification method from a clinical standpoint. The evaluation methods that are utilized in our work are the accuracy score and confusion matrix. The comprehensive experiments are conducted using the publicly accessible benchmark breast cancer dataset named as BreaKHis. As we have trained three models; first for binary classification and the other two for multiclass classification. We achieved 89.4% accuracy for our first model, 74.57%, and 57.41% accuracies for the remaining two multi-class models. These outcomes are astonishing as related to existing literature. As existing literature technique claims 88.9%, 63.6%, and 52.7% results against these models. The outcomes showed that the proposed methodology surpassed existing methods in breast cancer diagnosis by a significant margin. This is because of the Convolutional layers, we added before passing to the transformer in our proposed model. This convolution allows the features of the breast legion to be further prominent before applying the multi-head attention.