Breast cancer remains a global challenge, causing over 1 million deaths globally in 2018.
The models will be validated on validation and test sets of BCCD for full features and reduced features.
We specifically explore how a deep learning algorithm trained on screening mammograms from the US and UK generalizes to mammograms collected at a hospital in China, where screening is not widely implemented.
The performance of the proposed model is further compared with a linear-SVM model.
Weakly supervised instance labeling using only image-level labels, in lieu of expensive fine-grained pixel annotations, is crucial in several applications including medical image analysis.
At candidate level, AUC value of 0. 933 with 95% confidence interval of [0. 920, 0. 954] was obtained when symmetry information is incorporated in comparison with baseline architecture which yielded AUC value of 0. 929 with [0. 919, 0. 947] confidence interval.
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results.
Microwave imaging for breast cancer detection is based on the contrast in the electrical properties of healthy fatty breast tissues.
In this research work, a novel framework is pro- posed as an efficient successor to traditional imaging methods for breast cancer detection in order to decrease the computational complexity.