Evaluating Machine Learning Techniques for Breast Cancer Detection: A Comprehensive Review

Authors

Owen Kresse, Bioengineering Department, University of Louisville, USA
Youssef Kamel Kamel Hassan Rezk, Bioengineering Department, University of Louisville, USA
Alaaelddin Ibrahim Said, Bioengineering Department, University of Louisville, USA
Rana HossamEldin Mousa, Bioengineering Department, University of Louisville, USA
Merna Adel Abdelrahman Ibrahim, Bioengineering Department, University of Louisville, USA
Ahmed Fathy Sweed, Bioengineering Department, University of Louisville, USA
Tomas Pegorari, Bioengineering Department, University of Louisville, USA
Pablo Nakasato, Bioengineering Department, University of Louisville, USA
Ainhoa Osa-Sanchez, eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
Itxasne Del Barrio, eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
Francesc Serra Crespí, eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
Keltse Santisteban Ortiz, eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
Naiara Melián Eguia, eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
Mohamed Elsharkawy, Bioengineering Department, University of Louisville, USA
Ibrahim Abdelhalim, Bioengineering Department, University of Louisville, USA
Begonya Garcia-Zapirain, eVIDA Research Group, University of Deusto, 48007 Bilbao, Spain
Ayman El-Baz, Bioengineering Department, University of Louisville, USAFollow

Subject Area

Biomedical Engineering

Article Type

Review

Abstract

Breast cancer is considered one of the most common types of cancer among women. significant amount of effort done in early detection to increase survival chance since early detection is a challenging task especially in certain breast cancer conditions or using inefficient imaging modalities AI demonstrated significant potential in breast cancer detection algorithms including convolutional neural networks (CNNs) and Transformers, which have achieved highly accurate results but they have some limitations, such as the large amounts of data required for training as CNNs rely on local features, while Transformers focus on global features However, recent research has proposed hybrid models or modified attention as well as preprocessing techniques and transfer learning to solve this problem resulting in improved outcomes more challenges lie in data availability as there are limited datasets containing sufficient samples to train models effectively and the interpretability of models, as they are often treated as black boxes. Explainable AI is an active area of research in this paper we imaging modalities, disease statistics, publicly available datasets, and experimental AI models in detecting breast cancer including recent models.

Keywords

Breast Cancer Machine Learning Deep Learning

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Recommended Citation

Kresse, Owen; Rezk, Youssef Kamel Kamel Hassan; Said, Alaaelddin Ibrahim; Mousa, Rana HossamEldin; Ibrahim, Merna Adel Abdelrahman; Sweed, Ahmed Fathy; Pegorari, Tomas; Nakasato, Pablo; Osa-Sanchez, Ainhoa; Barrio, Itxasne Del; Crespí, Francesc Serra; Ortiz, Keltse Santisteban; Eguia, Naiara Melián; Elsharkawy, Mohamed; Abdelhalim, Ibrahim; Garcia-Zapirain, Begonya; and El-Baz, Ayman (2025) "Evaluating Machine Learning Techniques for Breast Cancer Detection: A Comprehensive Review," Mansoura Engineering Journal: Vol. 50 : Iss. 1 , Article 16.
Available at: https://doi.org/10.58491/2735-4202.3265