Ensemble classifier approach in breast cancer detection and malignancy grading- A review

TL;DR

This review discusses an ensemble classifier approach, EUSBoost, for early breast cancer detection and malignancy grading, combining image processing and machine learning to improve accuracy and address class imbalance issues.

Contribution

The paper introduces EUSBoost, an ensemble classifier that outperforms other methods by integrating boosting with random undersampling for breast cancer detection and grading.

Findings

EUSBoost outperforms other classifiers in accuracy.

The ensemble approach effectively handles class imbalance.

The system improves early detection and malignancy grading.

Abstract

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.