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.
Lymph node metastasis is one of the most significant diagnostic indicators in breast cancer, which is traditionally observed under the microscope by pathologists.
The combination of the radiologist's experience related to the BI-RADS and the morphological features leads to a more effective breast lesion classification.
Therefore EUSBoost, ensemble based classifier is proposed which is efficient and is able to outperform other classifiers as it takes the benefits of both-boosting algorithm with Random Undersampling techniques.
One of the most widely used feature extraction method is principle component analysis (PCA).