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A survey over deep learning methods for early detection in mammogram images

Authors :

Zeinab Shirkool¹ Mohammad Ali Tabarzad² Reza Boostani³

1- Islamic Azad University of Shiraz 2- Islamic Azad University of Shiraz 3- Shiraz University

Keywords :

Breast cancer،Deep learning،Convolutional neural networks،Graph neural networks،Transformer

Abstract :

Abstract—Breast cancer is the second most common cancer in the world. If cancerous masses are not detected in a proper time interval, they can jeopardize patients’ lives. Breast cancer can be detected in different ways, such as X-Ray, ultrasound, histopathological imaging, and genetic sequencing. Deep learning (DL) plays a vital role in medical imaging research, such as convolutional neural networks (CNNs), graph neural networks (GNNs), and transformer-based models. We also reviewed previous research on DL methods for detecting and classifying breast cancer. We focus on studies of previous DL methods, preprocessing, datasets, evaluation, and limitations. CNN and hybrid models increase performance compared to traditional machine learning. Transformer-based and graph-based models enhance feature representation. In this research, we use ScienceDirect, IEEE Xplore, Springer, and Google Scholar databases. This survey focuses on new DL-based architectures as well as multimodal fusion DL-based methods to make researchers familiar with state-of-the-art DL-based methods in breast cancer detection methods. We have briefly introduced the efficient DL-based schemes and compared their results on different publicly available datasets and discussed the pros and cons of the investigated methods.