Deep Learning Modeling Using Normal Mammograms for Predicting Breast Cancer

Breast cancer is the most dangerous leading cause of cancer-related death among women in the world. Internet of things (IoT) connected device that can help detect breast cancer earlier. Mammograms are the main way to detect breast cancer. But it is difficult to detect the cancer cell in the breast tissue. Because it has more tissue and less fat, the digitized mammography images are used for analyzing the abnormal areas of density, mass, and calcification that indicate the presence of cancer. In cases where adequate labeled data is available, deep learning approaches show considerable progress, and many advanced deep learning approaches have been proposed that have shown efficiency in the last few years in various modalities of computer vision and medical imaging. The small size of the medical image dataset also lowers the efficiency and robustness of computer-aided detection and/or diagnosis (CAD) systems based on deep learning. This research aims to build and test a new hybrid deep learning-based CAD method to predict the probability of a breast lesion found on a mammogram being malignant in an attempt to overcome this technological challenge. In this method, a deep convolutional neural network (CNN) will first be pre-trained using the ImageNet dataset. This work aims to develop and test a new CAD method based on hybrid transfer learning for mammographic mass classification. By combining the initial mammogram images with their morphological and texture variations, it is possible to produce more pseudo-color images to be added with information. Additional filters are used to remove unrelated transferred CNN characteristics on the network.