Recent advancements of deep learning in detecting breast cancer: a survey

Breast cancer is a deadly disease which is most commonly diagnosed in women. It spreads worldwide as the number of cases is increasing each day. The significant increase in cancer cases leads researchers to develop imaging tools for its detection. However, false-positive rates in manual detections are more, which may be due to human error, time taking process or some other issues. If these cancers are not detected at an early stage, they can cause the death of the patient. Therefore, several machine learning and deep learning-based methodologies have come into existence, which can be used to detect breast cancer in early stages. Nowadays, deep learning-based methods are trending, therefore, this paper discusses some important work done by researchers using deep learning. This survey provides an in-depth study of various deep learning models used for breast cancer detection. The details about mammography, histopathology and ultrasound datasets are also given which are being used for comparative analysis of the developed methods. This survey is not only on one imaging modality instead covering three modalities and thus giving a wider study of various methods. Important works from various years have been discussed along with recent works based on vision transformer. Therefore, this paper presents all the necessary information to researchers working in this area and they can think of new ideas based on it to carry out further work.

Automated summary

Breast cancer, a deadly disease primarily affecting women, is spreading globally with increasing cases. Manual detections suffer from high false-positive rates due to human error and time-consuming processes. Early detection is crucial to prevent fatality, hence the emergence of machine learning and deep learning approaches. This paper focuses on deep learning models for breast cancer detection, providing an in-depth study and analysis of mammography, histopathology, and ultrasound datasets. It encompasses a wider range of methods and recent works, including the use of vision transformers, serving as a valuable resource for researchers in the field.