Advanced Healthcare Systems

6Handling Unbalanced Data in Clinical Images

Amit Verma^*

School of Computer Science, UPES, Dehradun, Uttarakhand, India

Abstract

Manual detection of abnormalities accurately in the clinical images like MRIs by the operators is tedious work that requires good experience and knowledge, specifically manually segmenting the brain tumor in the MRI for further diagnosis by the doctor. So, multiple automatic and semi-automatic approaches were developed to automate the process of segmenting the malignant area of the tumor. The major problem which arises to train the model for automatic segmentation of clinical images is the imbalanced data set. An imbalanced clinical data set means the healthy tissues are always far greater than the cancerous tissues. This difference between the majority and minority data in the data sets reduces or adversely affects the accuracy of predicting model due to biased training data sets. So, it becomes a major concern for the various researchers to balance the data before using it to train a particular prediction model, and various data-level and algorithm–levelbased approaches were developed to balance the imbalance data for improving the accuracy of the trained model. In this chapter, the concept and problem of imbalanced data are discussed and various approaches for balancing the data are also highlighted in which one of the state-of-the-art method bagging is discussed in detail.

Keywords: Bagging, unbalanced data, boosting, MRI, deep learning, medical, ...