Pre-Trained Deep Convolutional Neural Network Architectures for Breast Cancer Diagnosis in Mammography: Current State-Of-The-Art

Abstract

Breast cancer is a prevalent and life-threatening disease affecting many women worldwide. Early diagnosis plays a crucial role in improving survival rates and allowing effective treatment. Mammography is recognized as the most effective imaging modality for breast cancer diagnosis. To achieve early diagnosis, researchers have developed intelligent systems based on Deep Learning (DL) models, specifically Convolutional Neural Networks (CNNs), which are well-suited for analyzing medical images. Training deep CNN models for breast cancer diagnosis poses challenges, mainly due to the limited size of publicly available mammography datasets commonly used by researchers. This scarcity of data can lead to overfitting issues. Obtaining a large-scale breast mammogram dataset is a time-consuming and expensive process in clinical practice. Researchers have successfully employed Transfer Learning (TL) to overcome this issue. In this scenario, models are initially trained on large datasets from other domains, generating pre-trained models that capture general image representations. These models are then fine-tuned on a breast cancer image dataset. This approach leverages the learned knowledge from the first dataset to improve the performance of DL models in the breast cancer diagnosis task. This survey aims to present the latest research findings in the realm of TL architectures applied to breast cancer diagnosis using limited mammography datasets. It covers the following areas: (i) The structure of CNNs, (ii) Essential background knowledge on TL, (iii) Diverse strategies for implementing TL, (iv) Commonly employed pre-trained CNN architectures, (v) Well-known publicly available mammography datasets along with their key characteristics and strengths, and (vi) A summary of the current state-of-the-art pretrained CNNs specifically applied to breast cancer diagnosis. The survey focuses on the performance and key findings of these models, showcasing their effectiveness in enhancing diagnostic accuracy. This survey seeks to summarize the current trends in TL for breast cancer diagnosis using mammography images, intending to inspire researchers to actively contribute to the progress of transfer learning in the mammogram image analysis field.