Different CNN-based Architectures for Detection of Invasive Ductal Carcinoma in Breast Using Histopathology Images

Abstract

In recent years, many improvements have been made in image processing techniques which aid pathologists to identify cancer cells. Nowadays, convolutional neural networks (CNNs), also known as deep learning algorithms have become popular for the applications of image processing and examination in histopathology image (tissue and cell images). This study aims to present the detection of histopathology images associated to detection of invasive ductal carcinoma (IDC) and non-IDC in breast. However, detection of IDC is a challenging task in histopathology image which needs deep examination as cancer comprises of minor entities with a diversity of forms which can be easily mixed up with different objects or facts contained in image. Hence, the proposed study suggests three types of CNN architectures which is called 8-layer CNNs, 9-layer CNNs and 19-layer CNNs, respectively, in the detecting IDC using histopathology images. The purpose of the study is to identify IDC from histopathology images by taking proper layer in deep layer CNNs with the maximum accuracy, highest sensitivity, precision and least classification error. The result shows better performance for deep layer-convolutional neural networks architecture by using 19-layer CNNs.