ResNet: Detection of Invasive Ductal Carcinoma in Breast Histopathology Images Using Deep Learning

Detection of Invasive Ductal Carcinoma (IDC) is a difficult, time taking and mandatory job. Moreover, the precise detection of IDC is important to give proper medical treatment to the patients. In this medical situation, Deep Learning (DL) approach is much reliable. The evolution of Deep Learning is taken off in the past few years due to a rise in more and more data, better hardware and it supports with robust computational modelling algorithms to train a framework. Based on the advantage of DL, a framework is introduced to automatically detect IDC in Breast Histopathology Images (BHI) of Breast Cancer (BC) either as a malign or benign. This ResNet is provided with labelled BHI data to extract common features and trained to classify the future images thus able to detect the presence of IDC in breast tissue lesion image. It takes less effort, gives better accuracy within less time. Thus our framework helps to speed up a pathologist's work and provides diagnosis support.