Breast Cancer Diagnosis Using Artificial Intelligence Approaches: A Systematic Literature Review

Abstract

One of the most prevalent cancers in women is breast cancer. Early and accurate detection can decrease the mortality rate associated with breast cancer. Governments and health organizations emphasize the significance of early breast cancer screening since it is associated to a greater variety of available treatments and a higher chance of survival. Patients have the best chance of obtaining effective treatment when they are diagnosed early. The detection and diagnosis of breast cancer have involved using various image types and imaging modalities. Breast “infrared thermal” imaging is one of the imaging modalities., a screening instrument used to measure the temperature distribution of breast tissue, and even though it has not been used as extensively as mammography it demonstrated encouraging outcomes when utilized for early detection. It also has several advantages, as it is safe, painless, non-invasive, and inexpensive. The literature showed that the use of thermal images with deep neural networks improved the accuracy of early diagnosis of breast malformations. Therefore, in this paper, we aim to provide a systematic review of research efforts and state-of-the-art studies in the domain of breast cancer detection using AI techniques. The review highlighted different issues, such as using different imaging modalities and deep attention mechanisms with deep learning (DL), which proved to enhance detection accuracy.