Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review
Journal of Medical Internet Research2019Vol. 21(7), pp. e14464–e14464
Citations Over TimeTop 1% of 2019 papers
Abstract
From the literature, it can be found that heterogeneous breast densities make masses more challenging to detect and classify compared with calcifications. The traditional ML methods present confined approaches limited to either particular density type or datasets. Although the DL methods show promising improvements in breast cancer diagnosis, there are still issues of data scarcity and computational cost, which have been overcome to a significant extent by applying data augmentation and improved computational power of DL algorithms.
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