Abstract
Abstract Globally, breast cancer remains a significant cause to female mortality. Early detection plays a crucial role in reducing premature deaths. Various imaging techniques, including ultrasound, mammogram, magnetic resonance imaging, histopathology, thermography, positron emission tomography, and microwave imaging, have been explored for obtaining breast images. This review article delves into the details of different breast imaging modalities and publicly accessible sources of breast images. The advanced machine learning (ML) technique offers a promising avenue to replace human involvement in detecting cancerous cells from breast images. The article outlines various ML algorithms (MLAs) extensively employed for identifying cancerous cells in images at the early stages, categorizing them based on the presence or absence of malignancy. Additionally, the review addresses current challenges associated with the application of MLAs in breast cancer identification and proposes potential solutions.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of Engineering and Science in Medical Diagnostics and Therapy
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
