Mammogram breast cancer CAD systems for mass detection and classification: a review

Although there is an improvement in breast cancer detection and classification (CAD) tools, there are still some challenges and limitations that need more investigation. The significant development in machine learning and image processing techniques in the last ten years affected hugely the development of breast cancer CAD systems especially with the existence of deep learning models. This survey presents in a structured way, the current deep learning-based CAD system to detect and classify masses in mammography, in addition to the conventional machine learning-based techniques. The survey presents the current publicly mammographic datasets, also provides a dataset-based quantitative comparison of the most recent techniques and the most used evaluation metrics for the breast cancer CAD systems. The survey provides a discussion of the current literature and emphasizes its pros and limitations. Furthermore, the survey highlights the challenges and limitations in the current breast cancer detection and classification techniques.

Automated summary

This survey presents a structured overview of current deep learning and traditional machine learning based CAD systems for breast cancer detection and classification. It provides information about publicly available mammographic datasets and evaluation metrics, and discusses the pros, limitations, challenges and limitations in the current literature.