A Multiple-Input Based Convolutional Neural Network in Breast Cancer Classification from Histopathological Images

Abstract

Breast cancer is considered the second most common reason for death among women. The gold standard for detecting breast cancer is the visual interpretation of histopathological images, but it is a complicated process that takes years of experience and a lot of skills of the pathologists. Sometimes, the limitations of the visual interpretation and the lack of experience result in the failure of diagnosing breast cancer. So, the computer-aided diagnosis (CAD) system can be taken into consideration as a helping tool to reduce the error of diagnosis of breast cancer. In this paper, a novel approach based on convolutional neural networks is introduced to classify breast cancer from the histopathological images of the breast tissues. In the clinical process of breast cancer diagnosis, pathologists examine the histopathological images of the breast tissue at different magnification levels. In this research, a single convolutional neural networks model is used to take the input of the same image with four different magnification levels parallelly. Our proposed approach outperformed existing state-of-the-art approaches by a substantial margin.