Applications of computational methods in biomedical Breast cancer imaging diagnostics: A review

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Date
2020Author
Aruleba, Kehinde
Obaido, George
Ogbuokiri, Blessing
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Show full item recordAbstract
With the exponential increase in new cases coupled with an increased mortality rate,
cancer has ranked as the second most prevalent cause of death in the world. Early detection is
paramount for suitable diagnosis and effective treatment of different kinds of cancers, but this is
limited to the accuracy and sensitivity of available diagnostic imaging methods. Breast cancer is
the most widely diagnosed cancer among women across the globe with a high percentage of total
cancer deaths requiring an intensive, accurate, and sensitive imaging approach. Indeed, it is treatable
when detected at an early stage. Hence, the use of state of the art computational approaches has been
proposed as a potential alternative approach for the design and development of novel diagnostic
imaging methods for breast cancer. Thus, this review provides a concise overview of past and present
conventional diagnostics approaches in breast cancer detection. Further, we gave an account of
several computational models (machine learning, deep learning, and robotics), which have been
developed and can serve as alternative techniques for breast cancer diagnostics imaging. This review
will be helpful to academia, medical practitioners, and others for further study in this area to improve
the biomedical breast cancer imaging diagnosis.