Breast Cancer Detection using Machine Learning

DOI

10.2991/978-94-6463-718-2_27How to use a DOI?

Keywords

Breast Cancer Detection; Machine Learning; Deep Learning; Support Vector Machine; K-Nearest Neighbors; Tumor Heterogeneity; Ultrasound Imaging; MRI; Federated Learning; Early Detection; Personalized Treatment; Artificial Intelligence

Abstract

Significant improvements have appeared in breast cancer detection by using machine learning (ML) as well as deep learning (DL) technologies. They are very useful in enhancing the efficiency, accuracy and accessibility of breast cancer detection. Various machine learning models, including support vector machines (SVM), k-nearest neighbors (K-NN), and deep learning algorithms, have been widely used to improve the prediction and recurrence detection of breast cancer. With improved precision and resilience to dense breast tissue or tumor heterogeneity, systems based on machine learning and trained on data from different diagnostic instruments (mammography, ultrasound images, and MRI) have shown promising results in breast cancer detection. Moreover, AI-based pre-screening methods support clinicians by decreasing diagnostic workloads and enabling early detection, including in low-resource environments. In addition, having ultrasound-based deep learning models offers a novel non-invasive and readily-available adjunctive method of assessing breast cancer; thus, making breast cancer screening more prevalent. Federated learning methods enable data-sharing across institutions in a collaborative manner, improving model generalizability while preserving patient privacy. The quantum leap that machine learning brings to the diagnosis of breast cancer, individualized treatment strategies, better patient outcomes and substantial cost savings for the healthcare system.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - M. Jayanthi
AU  - S. Ajith Kumar
AU  - P. Priyadharshi
AU  - S. Sanjana
AU  - G. Praveennayagam
AU  - N. Saravanakumar
PY  - 2025
DA  - 2025/05/23
TI  - Breast Cancer Detection using Machine Learning
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 302
EP  - 316
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_27
DO  - 10.2991/978-94-6463-718-2_27
ID  - Jayanthi2025
ER  -