REVIEW ARTICLE


Breast Cancer Detection Methodologies using Image Processing: Current Trends and Era in Machine Learning and Risk Mitigation



Kritika Raj Sharma1, Bhawna Goyal1, *, Mili Gupta2, Tripti Sharma1, Ayush Dogra3
1 Department of Electronics & Communication Engineering, Chandigarh University, Mohali 140413, India
2 Department of Biochemistry, Dr Harvansh Singh Judge Institute of Dental Sciences, PU, Chandigarh, India
3 Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India


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Creative Commons License
© 2023 Sharma et al.

open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

* Address correspondence to this author at the Department of Electronics & Communication Engineering, Chandigarh University, Mohali 140413, India; E-mail: bhawnagoyal28@gmail.com


Abstract

Breast cancer is a potentially fatal disease for its sufferers. Treatment is beneficial in the event of an early diagnosis. Medical imaging is critical in detecting breast cancer early and accurately; nevertheless, it is plagued by false negative and false positive findings, which frequently lead to wrong diagnosis and therapy. The response time is also lengthy, resulting in delay in treatment for patients whose lives could have been saved if the condition had been recognised sooner. Increasing survival rates and decreasing treatment-related side effects of breast cancer have long been established as a goal of screening programs. Artificial intelligence is a vast discipline with numerous algorithms that improve breast cancer detection imaging modalities' selectivity, specificity, and accuracy. In the initial sections of the paper, various risk factors for the disease are highlighted, followed by an in-depth study of existing work related to different imaging processes involved in breast cancer detection. In the later sections of the paper, some very new deep learning algorithms are mentioned with their achievements in breast cancer detection. Various data sets available are also tabulated in this research paper with precision.

Keywords: Breast cancer, BIRADS, Artificial intelligence AI, Convolutional neural networks CNN, Digital mammographs DM, Precision, Magnetic resonance imaging MRI, Digital breast tomosynthesis.