  Invasive ductal carcinoma is a prevalent, potentially deadly disease
associated with a high rate of morbidity and mortality. Its malignancy is the
second leading cause of death from cancer in women. The mammogram is an
extremely useful resource for mass detection and invasive ductal carcinoma
diagnosis. We are proposing a method for Invasive ductal carcinoma that will
use convolutional neural networks (CNN) on mammograms to assist radiologists in
diagnosing the disease. Due to the varying image clarity and structure of
certain mammograms, it is difficult to observe major cancer characteristics
such as microcalcification and mass, and it is often difficult to interpret and
diagnose these attributes. The aim of this study is to establish a novel method
for fully automated feature extraction and classification in invasive ductal
carcinoma computer-aided diagnosis (CAD) systems. This article presents a tumor
classification algorithm that makes novel use of convolutional neural networks
on breast mammogram images to increase feature extraction and training speed.
The algorithm makes two contributions.
