Abstract
Breast cancer is the most common cancer in women worldwide, accounting for more than 25% of all cancer cases and affecting more than 2.1 million people each year. According to the WHO, early detection is crucial to improving patient outcomes and survival. However, prognosis by histology of biopsy tissue is a complex procedure and the ultimate interpretation can be controversial. Therefore, machine learning algorithms are deployed to generate techniques that can be used by technicians, radiologists, and physicians as tools to unequivocally detect and diagnose breast cancer at an early stage. This will help to significantly increase the survival rate of the patients and their subsequent quality of life.
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