  The diagnosed cases of Breast cancer is increasing annually and unfortunately
getting converted into a high mortality rate. Cancer, at the early stages, is
hard to detect because the malicious cells show similar properties (density) as
shown by the non-malicious cells. The mortality ratio could have been minimized
if the breast cancer could have been detected in its early stages. But the
current systems have not been able to achieve a fully automatic system which is
not just capable of detecting the breast cancer but also can detect the stage
of it. Estimation of malignancy grading is important in diagnosing the degree
of growth of malicious cells as well as in selecting a proper therapy for the
patient. Therefore, a complete and efficient clinical decision support system
is proposed which is capable of achieving breast cancer malignancy grading
scheme very efficiently. The system is based on Image processing and machine
learning domains. Classification Imbalance problem, a machine learning problem,
occurs when instances of one class is much higher than the instances of the
other class resulting in an inefficient classification of samples and hence a
bad decision support system. 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. Also comparison of EUSBoost with other techniques is shown in the
paper.
